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Regressions

Linear Regression Blend Candles [Adaptive]Regression Blend Candles A hybrid candle system that blends standard OHLC candles with linear regression candles at a user-defined ratio. The result is a cleaner price representation that filters noise while preserving market structure. Adaptive modes automatically adjust the blend based on market conditions. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗛𝗢𝗪 𝗜𝗧 𝗪𝗢𝗥𝗞𝗦 The indicator calculates linear regression values for each OHLC component over a lookback period, then blends them with regular candle values based on your blend percentage. At 0% you see pure price action; at 100% you see full regression candles; anything between gives you a mix. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗙𝗘𝗔𝗧𝗨𝗥𝗘𝗦 ◽ Blend Control Set a fixed blend percentage or enable adaptive mode. The blend slider lets you dial in exactly how much smoothing you want—useful for finding the sweet spot between noise reduction and signal responsiveness. ◽ Adaptive Blend Modes Let the market decide the blend ratio: • ATR — Higher volatility increases LR blend to filter chop • StdDev — Similar concept using standard deviation • ATR + StdDev — Combines both volatility measures • R-Squared — Increases blend when price fits a linear trend well (high R² = clean trend = trust the regression more) • R² + ATR — Combines trend quality with volatility for a balanced approach ◽ R-Squared Thresholds Fine-tune when the R² adaptive mode kicks in. Below the low threshold, blend stays at minimum. Above the high threshold, blend reaches maximum. This prevents over-smoothing during choppy, non-linear price action. ◽ Post-LR Smoothing Apply additional smoothing to the regression values before blending: • ALMA — Arnaud Legoux Moving Average with offset/sigma control • Kalman — Adaptive filter that balances responsiveness and smoothness • KAMA — Kaufman Adaptive MA that adjusts to market efficiency ◽ Advanced LR Mode Enable weighted regression with exponential decay (emphasizes recent bars) and lag correction (extrapolates based on velocity to reduce inherent LR lag). ◽ Ghost Candles Display faded regular candles behind the blended candles to visualize the difference and spot divergences between raw price and the smoothed representation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 𝗦𝗨𝗚𝗚𝗘𝗦𝗧𝗘𝗗 𝗦𝗘𝗧𝗨𝗣𝗦 𝟭. 𝗧𝗿𝗲𝗻𝗱 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 (𝗦𝘄𝗶𝗻𝗴 𝗧𝗿𝗮𝗱𝗶𝗻𝗴) • LR Lookback: 14 • Blend %: 60-70% • Smoothing: None • Ghost Candles: On Use for cleaner swing identification. The higher blend percentage filters out intrabar noise while ghost candles let you see when price deviates significantly from the smoothed trend—potential reversal or continuation signals. 𝟮. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗡𝗼𝗶𝘀𝗲 𝗙𝗶𝗹𝘁𝗲𝗿 (𝗜𝗻𝘁𝗿𝗮𝗱𝗮𝘆) • LR Lookback: 10 • Adaptive Blend: On • Mode: R² + ATR • Min/Max Blend: 25% / 75% • R² Thresholds: 0.3 / 0.8 Ideal for intraday trading on volatile instruments. The blend automatically increases during clean trends (high R²) and volatile moves (high ATR), then backs off during choppy consolidation to keep you closer to raw price action when the regression isn't fitting well. 𝟯. 𝗨𝗹𝘁𝗿𝗮-𝗦𝗺𝗼𝗼𝘁𝗵 (𝗛𝗶𝗴𝗵𝗲𝗿 𝗧𝗶𝗺𝗲𝗳𝗿𝗮𝗺𝗲 𝗕𝗶𝗮𝘀) • LR Lookback: 20 • Blend %: 80% • Smoothing: ALMA (offset 0.85, sigma 6) • Advanced LR: On (decay 0.9, lag correction 1.5) Maximum smoothing for identifying higher timeframe directional bias. The combination of longer lookback, high blend, ALMA smoothing, and lag correction creates a highly filtered view that cuts through noise. Best used on 4H+ charts or as a trend filter for lower timeframe entries.

Indicateur Pine Script®

par B3AR_Trades

Linear Regression Channels This indicator dynamically identifies and plots the best-fit linear regression channels based on recent pivot points, optimizing for statistical strength across user-defined depths. How It Works (Technical Methodology) 1. Pivot Point Detection The indicator uses Pine Script's ta.pivothigh() and ta.pivotlow() functions with a configurable sensitivity length to detect swing highs and lows. All recent pivot indices are stored in an array (limited to avoid performance issues), providing potential starting points for regression calculations. 2. Multi-Depth Evaluation Users input comma-separated "Pivot History Depths" (e.g., "5,20,50"). For each depth: - The script evaluates regression fits starting from the most recent pivots, up to the specified depth count. - It calculates linear regression statistics for each possible channel originating from those pivot bars backward to the current bar. 3. Linear Regression Calculation For each candidate channel: - Slope (m) and intercept (b) are computed using least-squares method. - R-squared (R²) measures goodness of fit (how well price follows the trend line). - Standard error of the estimate is calculated to quantify volatility around the regression line. - A composite score = R² × log(length) prioritizes stronger fits on longer periods. 4. Best-Fit Selection and Validation - Only channels with R² ≥ user-defined minimum (default 0.5) are considered valid. - The channel with the highest score for each depth is selected and drawn. - This ensures the most statistically significant and relevant channels are displayed, avoiding weak or short-term noise. 5. Channel Construction - Mean Line: The regression trend line extended slightly into the future. - Inner Channels: ± user-configurable standard deviation multiplier (default 2.0σ) around the mean. - Outer Bands: ±1.5× the inner deviation for additional visual context. - Filled areas between mean and inner channels for better visibility. - Color: Green shades for upward slopes (bullish trend), red shades for downward slopes (bearish trend). 6. Dashboard and Statistics - Optional table in the top-right corner displays for each depth: - Depth value - R² (colored green if >0.7, orange otherwise) - Slope (Beta) – positive blue for uptrend, red for downtrend - Current Z-Score: How many standard deviations the latest close is from the expected regression value (yellow if |Z| > 2) How to Use Regression channels help identify trending markets, potential mean reversion, and overextension. - Upward Channels (Green): Price above the mean may indicate strength; pullbacks to the mean or lower band offer long opportunities. Overextension above upper band could signal exhaustion. - Downward Channels (Red): Price below the mean may indicate weakness; rallies to the mean or upper band offer short opportunities. Overextension below lower band could signal capitulation. - High R² (>0.7): Strong trending channel – trade in direction of slope. - Low R²: Choppy/range-bound market – avoid trend-following trades. - Z-Score: |Z| > 2 suggests price is statistically overextended from the trend (potential reversion setup). - Multi-Depth: Smaller depths catch short-term trends; larger depths capture major trends. Use multiple for confluence across timeframes. Combine with volume, support/resistance, or other indicators for confirmation. Disclaimer This indicator is a technical analysis tool and should be used in conjunction with other forms of analysis. Past performance does not guarantee future results. Always use proper risk management.

Indicateur Pine Script®

Composite Index [Auto Signals]Composite Index Description (描述正文): Overview This is an enhanced version of the famous Composite Index (CI) developed by Connie Brown. While the traditional RSI is confined between 0 and 100, often masking true momentum in strong trends, the Composite Index is uncapped and incorporates a momentum component to reveal the market's true structural strength. I have engineered this script to include Automated Signal Markers based on the crossover of the Composite Index and its Slow Moving Average. This helps traders instantly identify momentum shifts and "Timing" entries/exits without manual guesswork. Key Features Uncapped Momentum: Unlike RSI, the CI can go anywhere, preventing the "flattening" effect seen in strong trending markets (e.g., TSLA, NVDA). Automated Signals: ▲ Green Triangle (Launch): Triggers when the Gray CI line crosses ABOVE the Red Slow MA. This indicates bearish momentum is exhausted and bulls are regaining control. ▼ Red Triangle (Warning): Triggers when the Gray CI line crosses BELOW the Red Slow MA. This indicates bullish momentum is failing, serving as an early warning for exits or tightening stops. Classic Formula: Uses the standard Connie Brown parameters (14, 9, 3) + SMA smoothing for reliable divergence detection. How to Use This Indicator This script is best used as a companion to trend indicators like TTM Squeeze or Moving Average Ribbons. For Entries (The "Dip Buy"): In an uptrend, wait for a pullback. When the Green Triangle (▲) appears, it confirms that the pullback is over and momentum has turned back up. For Exits (The "Top"): Look for Divergence. If Price makes a Higher High but the Composite Index makes a Lower High—followed by a Red Triangle (▼)—this is a high-probability sell signal. The "Slow MA" Filter: The signals are generated only when the CI crosses the Slow MA (Red Line). This filters out the noise of minor fluctuations (crossing the Green line) and focuses on significant momentum changes. Settings RSI Period: 14 (Default) Momentum Period: 9 (Default) Signal Logic: Crossover/Crossunder of the Slow MA (33 Period). Disclaimer This tool is for educational purposes only. Always combine momentum signals with price action and structure analysis.

Indicateur Pine Script®

Box Indicator - Auto Draw Previous Day's - High / Midline / Low This indicator draws a box around the previous day’s high and low, calculates the midline, and displays them on the current day’s chart. It helps visualize key support/resistance levels from the prior trading day. This script gives you a static reference box from the prior day’s trading range, including a midpoint. It’s useful for spotting potential reversal zones, breakout levels, or intraday targets based on yesterday’s price action.

Indicateur Pine Script®

Shiori TFGI Lite Technical Fear and Greed Index (Open Source)Shiori’s TFGI Lite Technical Fear & Greed Index (Open Source) --- English — Official Description Shiori’s TFGI Lite is an open-source Technical Fear & Greed Index designed to help traders and investors understand market emotion, not predict price. Instead of generating buy or sell signals, this indicator focuses on answering a calmer, more important question: > Is the market emotionally stretched away from its own historical balance? TFGI Lite combines three well-known technical dimensions — volatility, price deviation, and momentum — and normalizes them into a single, intuitive 0–100 sentiment scale. What This Indicator Is * A market context tool, not a trading signal * A way to observe emotional extremes and misalignment * Designed for any asset, any timeframe * Fully open source, transparent and adjustable Core Components * Fear Factor: Short-term vs long-term ATR ratio with logarithmic compression * Greed Factor: Price Z-score with tanh-based normalization * Momentum Factor: Classic RSI as emotional momentum These factors are blended and gently smoothed to form the current sentiment level. Historical Baseline & Deviation TFGI Lite introduces a historical baseline concept: * The baseline represents the market’s own emotional equilibrium * Deviation measures how far current sentiment has drifted from that equilibrium This allows the indicator to highlight conditions such as: * 🔥 Overheated: High sentiment + strong positive deviation * 💎 Undervalued: Low sentiment + strong negative deviation * ⚠️ Misaligned: Emotionally extreme, but inconsistent with historical behavior How to Use (Lite Philosophy) * Use TFGI Lite as a background compass, not a trigger * Combine it with price structure, risk management, and your own strategy * Extreme readings suggest emotional tension, not immediate reversal > Think of TFGI Lite as market weather — it tells you the climate, not when to open or close the door. About Parameters & Customization All parameters in TFGI Lite are fully adjustable. Markets have different personalities — volatility, sentiment range, and emotional extremes vary by asset and timeframe. You are encouraged to: * Adjust fear/greed thresholds based on the asset you trade * Tune smoothing and baseline lengths to match your timeframe * Treat sentiment levels as relative, not universal absolutes There is no single “correct” setting — TFGI Lite is designed to adapt to your market, not force the market into a fixed model. Important Notes * This is a technical sentiment indicator, not financial advice * No future performance is implied * Designed to reduce emotional decision-making, not replace it --- 🇹🇼 繁體中文 — 指標說明 Shiori’s TFGI Lite（技術型恐懼與貪婪指數）是一款開源的市場情緒指標，目的不是預測價格，而是幫助你理解市場當下的「情緒狀態」。與其問「現在該不該買或賣」，TFGI Lite 更關心的是： > 市場情緒是否已經偏離了它自己的歷史平衡？本指標整合三個常見但關鍵的技術面向，並統一轉換為 0–100 的情緒刻度，讓市場狀態一眼可讀。這個指標是什麼 * 市場情緒與狀態觀察工具（非買賣訊號） * 用來辨識情緒極端與錯位狀態 * 適用於任何商品與任何週期 * 完全開源，可學習、可調整核心構成 * 恐懼因子：短期 / 長期 ATR 比例（對數壓縮） * 貪婪因子：價格 Z-Score（tanh 正規化） * 動能因子：RSI 作為情緒動量歷史基準與偏離 TFGI Lite 引入「歷史情緒基準」的概念： * 基準代表市場長期的情緒平衡 * 偏離值顯示當前情緒與自身歷史的距離因此可以辨識： * 🔥 過熱（高情緒 + 正向偏離） * 💎 低估（低情緒 + 負向偏離） * ⚠️ 錯位（情緒極端，但不符合歷史行為）使用建議（Lite 精神） * 將 TFGI Lite 作為「背景雷達」，而非進出場依據 * 搭配價格結構、風險控管與個人策略 * 情緒極端不等於立刻反轉 > 你可以把它想像成市場的天氣預報，而不是交易指令。參數調整與個人化說明本指標中的所有參數皆可調整。不同市場、不同商品，其波動特性與情緒區間並不相同。建議你： * 依標的特性自行調整恐懼 / 貪婪門檻 * 依交易週期調整平滑與基準長度 * 將情緒數值視為「相對狀態」，而非固定答案 TFGI Lite 的設計初衷，是讓你定義市場，而不是被單一參數綁住。溫馨提示如果你在調整指標參數時遇到不熟悉的項目，請點擊參數旁邊的「!」圖示，每個設定都有清楚的說明。本指標設計為可慢慢探索，請依自己的節奏理解市場狀態。 --- 🇯🇵 日本語 — インジケーター説明 Shiori’s TFGI Lite は、価格を予測するための指標ではなく、市場の「感情状態」を可視化するためのオープンソース指標です。この指標が問いかけるのは、 > 現在の市場感情は、過去のバランスからどれだけ乖離しているのか？という一点です。特徴 * 売買シグナルではありません * 市場心理の極端さやズレを観察するためのツールです * すべての銘柄・時間軸に対応 * 学習・調整可能なオープンソース構成要素 * 恐怖要素：ATR 比率（対数圧縮） * 強欲要素：価格 Z スコア（tanh 正規化） * モメンタム：RSI ベースラインと乖離市場自身の感情的な基準点と、現在の感情との距離を測定します。過熱・割安・感情のズレを視覚的に把握できます。パラメータ調整について TFGI Lite のすべてのパラメータは調整可能です。市場ごとにボラティリティや感情の振れ幅は異なります。 * 恐怖・強欲の閾値は銘柄に応じて調整してください * 時間軸に合わせて平滑化やベースライン期間を変更できます * 数値は絶対値ではなく、相対的な感情状態として捉えてくださいこの指標は、市場に合わせて柔軟に使うことを前提に設計されています。フレンドリーヒント入力項目で分からない設定がある場合は、横に表示されている「!」アイコンをクリックしてください。各パラメータには分かりやすい説明が用意されています。このインジケーターは、落ち着いて市場の状態を理解するためのものです。 --- 🇰🇷 한국어 — 지표 설명 Shiori’s TFGI Lite는 매수·매도 신호를 제공하는 지표가 아니라, 시장 감정의 상태를 이해하기 위한 기술적 심리 지표입니다. 이 지표의 핵심 질문은 다음과 같습니다. > 현재 시장 감정은 과거의 균형 상태에서 얼마나 벗어나 있는가? 특징 * 거래 신호 아님 * 시장 심리의 과열·저평가·불일치를 관찰 * 모든 자산, 모든 타임프레임 지원 * 오픈소스 기반 구성 요소 * 공포 요인: ATR 비율 (로그 압축) * 탐욕 요인: Z-Score (tanh 정규화) * 모멘텀: RSI 활용 방법 TFGI Lite는 배경 지표로 사용하세요. 가격 구조와 리스크 관리와 함께 사용할 때 가장 효과적입니다. 파라미터 조정 안내 TFGI Lite의 모든 설정 값은 사용자가 직접 조정할 수 있습니다. 자산마다 변동성과 감정 범위는 서로 다릅니다. * 공포 / 탐욕 기준값은 종목 특성에 맞게 조정하세요 * 타임프레임에 따라 스무딩 및 기준 기간을 변경할 수 있습니다 * 감정 수치는 절대적인 값이 아닌 상대적 상태로 해석하세요 이 지표는 하나의 정답을 강요하지 않고, 시장에 맞춰 적응하도록 설계되었습니다. 친절한 안내 설정 값이 익숙하지 않다면, 항목 옆에 있는 "!" 아이콘을 클릭해 보세요. 각 입력값마다 설명이 제공됩니다. 이 지표는 천천히 시장의 맥락을 이해하도록 설계되었습니다. --- Educational purpose only. Not financial advice. --- #FearAndGreed #MarketSentiment #TradingPsychology #TechnicalAnalysis #OpenSourceIndicator #Volatility #RSI #ATR #ZScore #MultiAsset #TradingView #Shiori

Indicateur Pine Script®

Momentum Marks - Buy and Sell Indicators Indicator Overview This tool is a multi‑factor entry signal system designed to highlight potential BUY and SHORT opportunities directly on the chart with hard‑anchored labels. It combines trend, momentum, volatility, and volume conditions to reduce noise and provide more reliable trade signals. Core Components - EMA Trend Filter - Uses a fast EMA (9) and a slow EMA (21) to determine short‑term vs. medium‑term trend direction. - Signals only trigger when price aligns with the EMA relationship (e.g., fast above slow for shorts, fast below slow for buys). - RSI Extremes - RSI thresholds (default 65/35) ensure signals occur only when momentum is stretched into overbought or oversold zones. - Helps avoid false triggers during neutral conditions. - Linear Regression Channel - A regression line with ±2 standard deviation bands defines dynamic support and resistance. - Signals require price to be near the top (for shorts) or bottom (for buys) of the channel, adding a structural filter. - TTM Squeeze Histogram - Measures momentum shifts by comparing price to its EMA. - Signals require histogram confirmation: weakening momentum for shorts, strengthening momentum for buys. - Volume Confirmation - Volume must fade for shorts or surge for buys relative to a 20‑period average. - Ensures signals align with participation strength. Visual Output - Red “SHORT” label above bars when all short conditions align. - Green “BUY” label below bars when all buy conditions align. - Optional plotshape arrows (triangles) as backup markers. - Linear regression channel shaded between upper and lower bands. - EMA lines plotted for trend context. Key Features - Hard‑anchored labels: Signals are locked to confirmed bars, preventing repainting or shifting. - Multi‑layer confirmation: Requires trend, momentum, volume, and structure to align before firing. - Customizable inputs: Users can adjust EMA lengths, RSI thresholds, regression length, and squeeze parameters.

Indicateur Pine Script®

Vietnamese Stock: Discount Linear Regression Liquidity Grab The Discount Linear Regression Liquidity Grab is a sophisticated technical analysis tool that combines statistical trend analysis with Premium/Discount Zone and Price Action logic. Unlike standard Linear Regression Channels that repaint or stretch indefinitely, this indicator is dynamic: it automatically detects volatility breakouts to "reset" the channel, creating distinct market "Sections." This tool is designed to help traders identify trend exhaustion, fair value gaps (FVGs), and high-probability reversal or continuation zones using two distinct built-in strategies. Key Features 1. Dynamic Channel Resets The core engine calculates a Linear Regression Channel based on a Pearson R coefficient and Deviation multipliers. - How it works: When price breaks out of the Upper or Lower Deviation bands, the script recognizes a shift in momentum. It "locks" the previous channel and begins calculating a new one from the breakout point. - Benefit: This creates a historical map of market structure, showing you exactly where previous trends began and ended. 2. Smart Money Concepts (SMC) Integration For every completed section (channel), the indicator automatically highlights: Highest High & Lowest Low Boxes: Identifies the structural range of the previous move. - Gaps & FVGs: Automatically draws boxes for Fair Value Gaps and Price Gaps within the channel, acting as potential magnets for price. 3. The Discount Zone (New Feature) The indicator projects a Discount Area (Red Box) from the previous section's midline down to its lowest low. - Logic: This box represents the "Discount" pricing relative to the previous move. - Behavior: The box extends to the right until price successfully "grabs liquidity" (closes below the midline/red line). Once the grab occurs, the box stops extending, marking that the liquidity event is complete. Built-In Strategies This indicator includes two automated strategy signals based on the interaction between current price and historical sections. Strategy 1: Breakout & Retest (Trend Continuation) This strategy looks for a classic resistance-turned-support setup. - Breakout: Price closes above the Highest High of a previous section (Triangle Up). - Retest: Price pulls back and closes at or below that breakout level (Triangle Down). - Confirmation: Price breaks above the high of the initial breakout candle (Green Background). Strategy 2: Midline Reclaim (Mean Reversion / Discount Buy) This strategy focuses on buying from the "Discount" zone. - Liquidity Grab: Price drops below the Midline (Red Line) of a previous section, entering the Discount Zone. - Reclaim: Price closes back above the Midline, signaling that the dip was bought up. Signal: A Diamond shape and Teal Background appear. How to Use - Trend Trading: Use the Dynamic Channels to visualize the current slope. If the channel is angling up, look for long setups. - Confluence: Use the Discount Zones and FVG boxes as areas of interest. If price enters a Red Discount Box and forms a reversal pattern, it is a high-probability entry. - Stop Loss Placement: The Lowest Low boxes of previous sections serve as excellent invalidation points for long positions. Alerts The indicator comes with pre-configured alerts for: - Strategy 1 Confirmation. - Strategy 2 Midline Reclaim. - New Channel Formation (Trend Reset). - Liquidity Grab Events.

Indicateur Pine Script®

Ratio with Lag • Ratio = X(T) / Y(T-lag) • Auto-detects “X/Y” typed in chart search bar • Plots ratio directly on main chart • Adds 30-week MA (weekly SMA of the ratio) • Adds 150-day SMA (daily SMA of the ratio)

Indicateur Pine Script®

par KgA-Capital

Trend Flip Exhaustion Signals This Pine Script is designed to generate buy and short trading signals based on a combination of technical indicators. It calculates fast and slow EMAs, RSI, a linear regression channel, and a simplified TTM squeeze histogram to measure momentum. - Short signals trigger when price is above both EMAs, near the upper regression channel, momentum is weakening, volume is fading, and RSI is overbought. - Buy signals trigger when price is below both EMAs, near the lower regression channel, momentum is strengthening, volume is surging, and RSI is oversold. - Signals are displayed as labels anchored to price bars (with optional plotshape arrows for backup). - The script also plots the EMAs and regression channel for visual context. In short - it’s a trend‑following entry tool that highlights potential exhaustion points for shorts and potential reversals for buys, with clear on‑chart markers to guide decision‑making.

Indicateur Pine Script®

Physics of Price Physics of Price is a non-repainting kinematic reversal and volatility overlay. It models price as a physical object with position, velocity, and acceleration, then builds adaptive bands and a short-term predictive “ghost cone” to highlight where reversals are statistically more likely. CONCEPT Instead of using only moving averages, the core engine tracks a smoothed price (position), trend speed (velocity), and change in trend speed (acceleration). Standard deviation of the model error defines probabilistic bands around this kinematic centerline. When price stretches too far away and snaps back, the move is treated as a potential exhaustion event. CORE COMPONENTS – Kinematic centerline (Alpha–Beta–Gamma style filter) that bends with trend instead of lagging like a simple MA. – Inner and outer bands based on the standard deviation of residuals between price and the kinematic model. – Regime filter using R² and band width to avoid signals in chaotic or ultra-wide regimes. – Optional RSI “hook” filter that waits for momentum to actually turn instead of buying into a falling RSI. – Optional divergence add-on using kinematic velocity, so a marginal new price extreme with weaker velocity is recognized as a possible exhaustion pattern. REVERSAL EVENTS AND SCORING Raw events are detected when price wicks through the outer band and closes back inside (band hit with snap). These are plotted as diamonds and treated as candidates, not automatic trades. Each event is then scored from 0 to 100 using several factors: – How far price overshot the outer band. – How strongly it snapped back inside. – Whether an RSI hook is present (if enabled). – Regime quality from the kinematic model. – Basic kinematic safety to avoid the most aggressive “knife-catch” situations. – Optional divergence bonus when price makes a new extreme but velocity does not. Only events with a score above the chosen threshold become confirmed signals (triangles labeled PHYSICS REV). GHOST CONE (PREDICTIVE BAND) On the latest bar, the script projects a short-horizon “ghost cone” into the future using position, velocity, and a damped acceleration term. This creates a curved predictive band that visualizes a plausible short-term path and range, rather than a simple straight line. The cone is meant as context for trade management and risk, not as a hard target. FILTERS AND OPTIONS – Regime filter (R² and band width) can be tightened or relaxed depending on how selective you want the engine to be. – RSI and volume filters can be toggled on for extra confirmation or off to see the raw kinematic behavior. – An optional trend baseline (EMA) can be enabled to bias or restrict reversals relative to a higher-timeframe trend. – Dynamic cooldown scales with volatility so the script does not spam signals in fast environments. HOW TO USE Physics of Price is primarily a mean-reversion and exhaustion tool. It works best in markets that respect ranges, swings, and two-sided order flow. Confirmed PHYSICS REV signals near the outer bands, with decent model health and a clean RSI hook, are the core use case. The bands and ghost cone can also be used as a context overlay alongside your own entries, exits, and risk framework. This is an indicator, not a complete trading system. It does not use lookahead or higher-timeframe security calls and is designed for “once per bar close” alerts. Always combine it with your own risk management and confluence.

Indicateur Pine Script®

Mis à jour

ADX Forecast Colorful [DiFlip]ADX Forecast Colorful Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis. ⯁ Real-Time ADX Forecasting For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions. ⯁ Highly Customizable The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system. ⯁ Innovative and Science-Based This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is: y = β₀ + β₁x + ε Where: y = predicted value (e.g., future ADX) x = explanatory variable (e.g., bar index or time) β₀ = intercept β₁ = slope (rate of change) ε = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends. ⯁ Least Squares Estimation To minimize the error, the regression coefficients are calculated as: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: Σ = summation x̄ and ȳ = means of x and y i ranges from 1 to n (number of data points) These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity. ⯁ Linear Regression in Machine Learning Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool. ⯁ Visual Interpretation Imagine an ADX time series like this: Time → ADX → The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals. ⯁ Summary of Scientific Concepts Linear Regression: Models variable relationships with a straight line. Least Squares: Minimizes prediction errors for best fit. Time-Series Forecasting: Predicts future values using historical data. Supervised Learning: Trains models to predict outcomes from inputs. Statistical Smoothing: Reduces noise and highlights underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Based on rigorous statistical theory. Unprecedented: First public ADX using least-squares forecast modeling. Smart: Uses machine learning logic. Forward-Looking: Generates predictive, not just reactive, signals. Customizable: Flexible for any strategy or timeframe. ⯁ Conclusion By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What is the ADX? The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ How to use the ADX? The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: Strong Trend: When the ADX is above 25, indicating a strong trend. Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. Neutral Zone: Between 20 and 25, where the trend strength is unclear. ⯁ Entry Conditions Each condition below is fully configurable and can be combined to build precise trading logic. 📈 BUY 🅰️ Signal Validity: The signal will remain valid for X bars . 🅰️ Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 📉 SELL 🅰️ Signal Validity: The signal will remain valid for X bars . 🅰️ Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 🤖 Automation All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems. ⯁ Unique Features Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill" Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill"

Indicateur Pine Script®

Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note The log-regression window LR length controls how long a long-term fair value path is estimated from historical data. The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”. When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as: “Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.” Guidelines: In general, set the AHR window equal to or slightly longer than the LR window: If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution. If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history. For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years). For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years. 1. Price series & windows Working timeframe: daily (1D). Let the daily close of the current symbol on day t be P_t . Main length parameters: HM window: L_HM = maLen (default 200 days) Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years) AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years) 2. Harmonic moving average (HM) On a window of length L_HM, define the harmonic mean: HM_t = ^(-1) Here eps = 1e-10 is used to avoid division by zero. Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly. 3. Log-regression baseline (LR) On a window of length L_LR, perform a linear regression on log price: Over the last L_LR bars, build the series x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit x_k ≈ a + b * k. The fitted value at the current index t is log_P_hat_t = a + b * t. Exponentiate to get the log-regression baseline: LR_t = exp( log_P_hat_t ). Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days. 4. HM-based AHR (valuation ratio) At each time t, build an HM-based AHR (valuation multiple): AHR_t = ( P_t / HM_t ) * ( P_t / LR_t ) Interpretation: P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean). P_t / LR_t : deviation of price from the long-term log-regression trend. Multiplying them means: if price is above both HM and LR, “expensiveness” is amplified; if price is below both, “cheapness” is amplified. Typical reading: AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper. AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive. 5. Empirical quantile thresholds (Opp / Risk) On each new day, whenever AHR_t is valid, add it into a rolling array: A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements) On this empirical distribution, define two quantiles: Opportunity quantile: q_opp (default 15%) Risk quantile: q_risk (default 65%) Using standard percentile computation (order statistics + linear interpolation), we get: Opp threshold: theta_opp = Percentile( A_t_window, q_opp ) Risk threshold: theta_risk = Percentile( A_t_window, q_risk ) We also compute the percentile rank of the current AHR inside the same history: q_now = PercentileRank( A_t_window, AHR_t ) ∈ This yields three valuation zones: Opportunity zone: AHR_t <= theta_opp (corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.) Neutral zone: theta_opp < AHR_t < theta_risk Risk zone: AHR_t >= theta_risk (corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.) All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity. 6. DCA simulation (lightweight, rolling window) Given: a daily budget B (input: budgetPerDay), and a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years), The script applies the following rule on each new day t: If thresholds are unavailable or AHR_t > theta_risk → classify as Risk zone → buy = 0 If AHR_t <= theta_opp → classify as Opportunity zone → buy = 2B (double size) Otherwise (Neutral zone) → buy = B (normal DCA) Daily invested cash: C_t ∈ {0, B, 2B} Daily bought quantity: DeltaQ_t = C_t / P_t The script keeps rolling sums over the last H days: Cumulative position: Q_H = sum_{k=t-H+1..t} DeltaQ_k Cumulative invested cash: C_H = sum_{k=t-H+1..t} C_k Current portfolio value: PortVal_t = Q_H * P_t Cumulative P&L: PnL_t = PortVal_t - C_H Active days: number of days in the last H with C_k > 0. These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.

Indicateur Pine Script®

par Anoria_4RICH

Omega Correlation [OmegaTools]Omega Correlation (Ω CRR) is a cross-asset analytics tool designed to quantify both the strength of the relationship between two instruments and the tendency of one to move ahead of the other. It is intended for traders who work with indices, futures, FX, commodities, equities and ETFs, and who require something more robust than a simple linear correlation line. The indicator operates in two distinct modes, selected via the “Show” parameter: Correlation and Anticipation. In Correlation mode, the script focuses on how tightly the current chart and the chosen second asset move together. In Anticipation mode, it shifts to a lead–lag perspective and estimates whether the second asset tends to behave as a leader or a follower relative to the symbol on the chart. In both modes, the core inputs are the chart symbol and a user-selected second symbol. Internally, both assets are transformed into normalized log-returns: the script computes logarithmic returns, removes short-term mean and scales by realized volatility, then clips extreme values. This normalisation allows the tool to compare behaviour across assets with different price levels and volatility profiles. In Correlation mode, the indicator computes a composite correlation score that typically ranges between –1 and +1. Values near +1 indicate strong and persistent positive co-movement, values near zero indicate an unstable or weak link, and values near –1 indicate a stable anti-correlation regime. The composite score is constructed from three components. The first component is a normalized return co-movement measure. After transforming both instruments into normalized returns, the script evaluates how similar those returns are bar by bar. When the two assets consistently deliver returns of similar sign and magnitude, this component is high and positive. When they frequently diverge or move in opposite directions, it becomes negative. This captures short-term co-movement in a volatility-adjusted way. The second component focuses on high–low swing alignment. Rather than looking only at closes, it examines the direction of changes in highs and lows for each bar. If both instruments are printing higher highs and higher lows together, or lower highs and lower lows together, the swing structure is considered aligned. Persistent alignment contributes positively to the correlation score, while repeated mismatches between the swing directions reduce it. This helps differentiate between superficial price noise and structural similarity in trend behaviour. The third component is a classical Pearson correlation on closing prices, computed over a longer lookback. This serves as a stabilising backbone that summarises general co-movement over a broader window. By combining normalized return co-movement, swing alignment and standard price correlation with calibrated weights, the Correlation mode provides a richer view than a single linear measure, capturing both short-term dynamic interaction and longer-term structural linkage. In Anticipation mode, Omega Correlation estimates whether the second asset tends to lead or lag the current chart. The output is again a continuous score around the range. Positive values suggest that the second asset is acting more as a leader, with its past moves bearing informative value for subsequent moves of the chart symbol. Negative values indicate that the second asset behaves more like a laggard or follower. Values near zero suggest that no stable lead–lag structure can be identified. The anticipation score is built from four elements inspired by quantitative lead–lag and price discovery analysis. The first element is a residual lead correlation, conceptually similar to Granger-style logic. The script first measures how much of the chart symbol’s normalized returns can be explained by its own lagged values. It then removes that component and studies the correlation between the residuals and lagged returns of the second asset. If the second asset’s past returns consistently explain what the chart symbol does beyond its own autoregressive behaviour, this residual correlation becomes significantly positive. The second element is an asymmetric lead–lag structure measure. It compares the strength of relationships in both directions across multiple lags: the correlation of the current symbol with lagged versions of the second asset (candidate leader) versus the correlation of lagged values of the current symbol with the present values of the second asset. If the forward direction (second asset leading the first) is systematically stronger than the backward direction, the structure is skewed toward genuine leadership of the second asset. The third element is a relative price discovery score, constructed by building a dynamic hedge ratio between the two prices and defining a spread. The indicator looks at how changes in each asset contribute to correcting deviations in this spread over time. When the chart symbol tends to do most of the adjustment while the second asset remains relatively stable, it suggests that the second asset is taking a greater role in determining the equilibrium price and the chart symbol is adjusting to it. The difference in adjustment intensity between the two instruments is summarised into a single score. The fourth element is a breakout follow-through causality component. The script scans for breakout events on the second asset, where its price breaks out of a recent high or low range while the chart symbol has not yet done so. It then evaluates whether the chart symbol subsequently confirms the breakout direction, remains neutral, or moves against it. Events where the second asset breaks and the first asset later follows in the same direction add positive contribution, while failed or contrarian follow-through reduce this component. The contribution is also lightly modulated by the strength of the breakout, via the underlying normalized return. The four elements of the Anticipation mode are combined into a single leading correlation score, providing a compact and interpretable measure of whether the second asset currently behaves as an effective early signal for the symbol you trade. To aid interpretation, Omega Correlation builds dynamic bands around the active series (correlation or anticipation). It estimates a long-term central tendency and a typical deviation around it, plotting upper and lower bands that highlight unusually high or low values relative to recent history. These bands can be used to distinguish routine fluctuations from genuinely extreme regimes. The script also computes percentile-based levels for the correlation series and uses them to track two special price levels on the main chart: lost correlation levels and gained correlation levels. When the correlation drops below an upper percentile threshold, the current price is stored as a lost correlation level and plotted as a horizontal line. When the correlation rises above a lower percentile threshold, the current price is stored as a gained correlation level. These levels mark zones where a historically strong relationship between the two markets broke down or re-emerged, and can be used to frame divergence, convergence and spread opportunities. An information panel summarises, in real time, whether the second asset is behaving more as a leading, lagging or independent instrument according to the anticipation score, and suggests whether the current environment is more conducive to de-alignment, re-alignment or classic spread behaviour based on the correlation regime. This makes the tool directly interpretable even for users who are not familiar with all the underlying statistical details. Typical applications for Omega Correlation include intermarket analysis (for example, index vs index, commodity vs related equity sector, FX vs bonds), dynamic hedge sizing, regime detection for algorithmic strategies, and the identification of lead–lag structures where a macro driver or benchmark can be monitored as an early signal for the instrument actually traded. The indicator can be applied across intraday and higher timeframes, with the understanding that the strength and nature of relationships will differ across horizons. Omega Correlation is designed as an advanced analytical framework, not as a standalone trading system. Correlation and lead–lag relationships are statistical in nature and can change abruptly, especially around macro events, regime shifts or liquidity shocks. A positive anticipation reading does not guarantee that the second asset will always move first, and a high correlation regime can break without warning. All outputs of this tool should be combined with independent analysis, sound risk management and, when appropriate, backtesting or forward testing on the user’s specific instruments and timeframes. The intention behind Omega Correlation is to bring techniques inspired by quantitative research, such as normalized return analysis, residual correlation, asymmetric lead–lag structure, price discovery logic and breakout event studies, into an accessible TradingView indicator. It is intended for traders who want a structured, professional way to understand how markets interact and to incorporate that information into their discretionary or systematic decision-making processes.

Indicateur Pine Script®

Multivariate Kalman Filter 🙏🏻 I see no1 ever posted an open source Multivariate Kalman filter on TV, so here it is, for you. Tested and mathematically correct implementation, with numerical safeties in place that do not affect the final results at all. That’s the main purpose of this drop, just to make the tool available here. Linear algebra everywhere, Neo would approve 4 sure. ... Personally I haven't found any real use case of it for myself, aside from a very specific one I will explain later, but others usually do… Almost every1 in the quant industry who uses Kalman is in fact misusing it, because by its real definition, it should be applied to Not the exact known values (e.g as real ticks provided by transparent audited regulated exchange), but “measurements” of those ‘with errors’. If your measurements don’t have errors or you have real precise data, by its internal formulas Kalman will output the exact inputs. So most who use it come up with some imaginary errors of sorts, like from some kind of imaginary fair price etc. The important easy to miss point, the errors of your measurements have to be symmetric around its mean ‘ at least ’, if errors are biased, Kalman won’t provide. For most tasks there are better tools, including other state space models , but still Multivariate Kalman is a very powerful instrument, you can make it do all kinds of stuff. Also as a state space model it can also provide confidence & prediction intervals without explicit calculations of dem. ... In this script I included 2 example use cases, the first one is the classic tho perfectly working misuse, the second one is what I do with it: One Naive, estimates “hidden” adaptive moving regression endpoint. The result you can see on the chart above. You can imagine that your real datapoints are in fact non perfect measures of some hidden state, and by defining measurement noise and process noise, and by constructing the input matrixes in certain ways, you can express what that state should be. Two Upscaling tick lattice, aka modelling prices as if native tick size would’ve been lower. Kinda very specific task, mostly needed in HFT or just for analytical purposes. Some like ZN have huge tick sizes, they are traded a lot but barely do more than 20 ticks range in a session. The idea is to model raw data as AR2 process , learn the phi1 and phi2, make one point forecasts based on dem, and the process noise would be the variance of errors from these forecasts. The measurement noise here is legit, it’s quantization noise based on tick size, no need in olympic gold in mental gymnastics xd ^^ artificially upscaling ZN futures tick lattice ... I really made it available there so You guys can take it and some crazy ish with it, just let state space models abduct your conciseness and never look back ∞

Indicateur Pine Script®

ArithmaReg Candles [NeuraAlgo]ArithmaReg Candles ArimaReg Candles provide a quantitative approach toward the visualization of price by rebuilding each candle using an adaptive regression model. This indicator eliminates much of the noise and micro-spikes and consolidates irregular volatility of raw OHLC data, which typically characterizes candles, into a much cleaner and more stable representation that better reflects the true directional intent of the market. The algorithm applies a dynamic state-space filter to track the equilibrium price, truePrice, while suppressing high-frequency fluctuations. Noise in the price is extracted by comparing the raw close to the filtered state and removed from the candle body and wick structure through controlled adjustment logic. Finally, a volatility-based spread model rebuilds the candle's range to maintain realistic price geometry. The direction of trends is given by comparing the truePrice against a smoothing baseline, permitting ArithmaReg Candles to underline the bullish and bearish phases with more clarity and much-reduced distortion. This yields a chart where transitions within trends, pullbacks, and momentum shifts are much easier to comprehend than their representation via traditional candles. ArithmaReg Candles are designed for traders who require consistent, noise-filtered price structure-ideal for trend analysis, breakout validation, and precision entries. The indicator itself does not generate any signals; it only refines the visual environment so that your existing tools and decision models become more reliable. How It Works Micro-Price Extraction A weighted micro-price is calculated to represent the bar's internal structure and reduce intrabar irregularities. Adaptive Regression Filter The state-based regression engine continuously updates price equilibrium, adjusting its confidence level. This gives the filter the ability to remain responsive during strong movements yet be stable during noisy periods. Noise Removal & Candle Reconstruction The difference between raw price and truePrice is considered noise. This noise is subtracted from OHLC values, and a volatility-scaled spread restores realistic wick and body proportions. What results is a candle that depicts true directional flow. Trend Classification A smoothed trend baseline is computed from the filtered price, and candle color is determined by whether the market is positioned above or below this equilibrium trend. How to Use It Identify True Trend Direction Candles follow the cleaned price path so that you can differentiate valid trend shifts from temporary spikes or wick-driven traps. Improve Existing Strategies These candles will complement your existing indicators, be they Supertrend, moving averages, volume tools, or momentum oscillators, by giving you a more sound price basis. Spot Clean Breakouts & Pullbacks Reduced noise makes breakout structure, swing highs/lows, and retracements significantly clearer. This is particularly useful in fast markets like crypto and Forex. Improve Entry & Exit Timing By highlighting the underlying flow of price, ArithmaReg Candles help traders avoid false signals and pinpoint spots where the price momentum is actually changing. Adaptable to All Timeframes & Assets The filter is self-adjusting, so it performs consistently on scalping timeframes, intraday charts, swing setups, and all asset classes. Summary ArithmaReg Candles create a mathematically refined view of market structure by removing noise and reconstructing candles through adaptive regression. The result is a more refined, stable price representation that improves trend recognition and decision-making and enables professional-grade technical analysis.

Indicateur Pine Script®

Sniper BB + VWAP System (with SMT Divergence Arrows)STEP 1: Load two correlated futures charts. Example: CL + RB/SI+GC/ NQ+ES STEP 2: Add Bollinger Bands (20, 2.0) on both. Optional add (20, 3.0). STEP 3: Watch for a BB tag on one chart but not the other. STEP 4: Wait for a reclaim candle back inside the band. STEP 5: Enter with stop below/above the wick + 3.0 BB. STEP 6: Scale out midline, then opposite band. STEP 7: Hold partials when both pairs confirm trend. *You can take the vwap bands off the chart if it is too cluttered.

Indicateur Pine Script®

par the1nepercent

Universal Scalper Indicator [Crypto/Forex/Gold]Universal Scalper Pro is an all-in-one scalping system designed for the 15-Minute Timeframe. It automates the analysis of trend, volatility, and risk management into a single, high-contrast dashboard. Unlike standard crossover indicators, this system filters out low-volatility "noise" using a built-in ADX engine and automatically calculates dynamic Stop Loss and Take Profit levels based on market volatility (ATR). It is engineered to work universally on: Crypto (BTC, ETH, SOL, Altcoins) Commodities (Gold, Silver, Oil) Forex (Major & Minor Pairs) Stocks (High volume tech stocks like NVDA, TSLA) 📈 How It Works (The Strategy) 1. The Trend Engine (9/21 EMA) The core logic utilizes a Fast (9) and Slow (21) Exponential Moving Average crossover. Bullish Signal: The 9 EMA crosses above the 21 EMA. Bearish Signal: The 9 EMA crosses below the 21 EMA. This specific combination is chosen for its responsiveness to 15-minute intraday trends. 2. The Noise Filter (ADX > 15) To prevent "whipsaws" (fake signals during sideways markets), the script includes a Volatility Filter based on the Average Directional Index (ADX). Signals are rejected if the ADX is below 15. This ensures you only receive alerts when there is sufficient momentum to sustain a move. 3. Dynamic Risk Management (ATR) The script uses the Average True Range (ATR) to calculate Stop Loss and Take Profit levels that adapt to the specific asset's volatility. Stop Loss: Placed at 1.5x ATR from the entry. (Tight enough to preserve capital, wide enough to survive standard market noise). Take Profit: Placed at 2.0x ATR from the entry. (Provides a healthy 1:1.3 Risk/Reward ratio). 🚀 Key Features Universal Dashboard: A bottom-right panel displays the live Trend Status, Entry Price, Stop Loss, and Take Profit. It automatically formats decimals for any asset (e.g., 2 decimals for Gold, 5 for Forex, 8 for Crypto). "Sticky" Memory: The dashboard retains the prices of the last valid signal, allowing you to manage your trade even after the signal candle closes. Trend Cloud: A visual Green/Red zone between the EMAs helps you instantly identify the market bias. Unified Alerts: A single alert setup ("Any alert() function call") sends the Asset Name, Entry, SL, and TP directly to your phone. 🛠️ How to Use Timeframe: Set your chart to 15 Minutes (15m). Wait for the Signal: Look for the "BUY" (Green) or "SELL" (Red) label on the chart. Check the Dashboard: Ensure the "STATUS" is BULLISH (for buys) or BEARISH (for sells). If the status says "WAIT", do not trade. Execute: Enter the trade using the exact Stop Loss and Take Profit levels shown on the dashboard. ⚠️ Risk Disclaimer Trading financial markets involves high risk and may not be suitable for all investors. This indicator is a technical analysis tool and does not constitute financial advice. Past performance is not indicative of future results. Always practice with a demo account before trading real capital.

Indicateur Pine Script®

par billal-kashif

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Total Returns indicator by PtahX PtahX Total Returns – True Total-Return View for Any Symbol Most charts only show price. This script shows what your position actually did once you include dividends and, optionally, inflation. What this indicator does 1. Builds a Total Return series You choose how dividends are treated: * Reinvest (default): All gross dividends are automatically reinvested into more shares on the ex-dividend bar. * Cash: Dividends are kept as cash added on top of your initial position. * Ignore: Price only, like a regular chart. This answers: “If I bought once at the start and held, how much would that position be worth now, given this dividend policy?” 2. Optional inflation-adjusted (real) returns You can also plot a real total-return line, which adjusts for inflation using a CPI series. This answers: “How did my purchasing power change after inflation?” 3. Stats window and exponential trendline You can pick the time window: * Since inception (full available history) * YTD * Last 1 Year * Last 5 Years * Custom start date For that window, the script: * Normalizes Total Return to 1.0 at the window start. * Fits an exponential trendline (pink) to the normalized series. * Displays a stats table in the bottom-right showing: • Overall Return (%) over the selected range • CAGR (compound annual growth rate, % per year) • Trendline growth (% per year) • R² of the trendline (fit quality) • A separate “Since inception” block (overall return and CAGR from the first bar on the chart) How to use it 1. Add the indicator to your chart. 2. Open the settings: Total Return & Dividends * Dividend mode • Reinvest: closest to a true total-return curve (default). • Cash: price plus cash dividends. • Ignore: price only. * Plot inflation-adjusted TR line • Turn this on if you want to see a real (CPI-adjusted) total-return line. Inflation / Real Returns * Inflation country code and field code • Leave defaults if you just want a standard CPI series. * Use real TR for stats & trendline • On: stats and trendline use the inflation-adjusted curve. • Off: stats use the nominal (non-adjusted) total return. Stats Range & Trendline * Stats range: Since inception, YTD, 1 Year, 5 Years, or Custom date. * Custom date: set year, month, and day if you choose “Custom date”. * Plot TR exponential trendline: show or hide the pink curve. * Show stats table / Show Overall Return / Show Trendline stats: toggle what appears in the table. 3. Zoom and change timeframe as usual. The stats range is based on calendar time (YTD, 1Y, 5Y, etc.), not bar count, so the numbers stay meaningful as you change resolutions. How to read the outputs * Teal line: Nominal Total Return (using your chosen dividend mode). * Orange line (if enabled): Real (inflation-adjusted) Total Return. * Pink line (if enabled): Exponential trendline for the selected stats window. On the right edge, small labels show the latest value of each active line. In the bottom-right stats table: * Overall Return: total percentage gain or loss over the chosen stats range. * CAGR: the smoothed annual rate that would turn 1.0 into the current value over that range. * Exponential Trendline: the average trendline growth per year and the R². • R² near 1 means prices follow a clean exponential path. • Lower R² means more noise or sideways movement around the trend. * Range: which window those stats apply to (YTD, 1Y, 5Y, etc.). * Since inception: overall return and CAGR from the first bar on the chart up to the latest bar, independent of the current stats range. Use this when you want to compare true performance, not just price – especially for dividend-heavy ETFs, funds, and income strategies.

Indicateur Pine Script®

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Donchian Predictive Channel (Zeiierman)█ Overview Donchian Predictive Channel (Zeiierman) extends the classic Donchian framework into a predictive structure. It does not just track where the range has been; it projects where the Donchian mid, high, and low boundaries are statistically likely to move based on recent directional bias and volatility regime. By quantifying the linear drift of the Donchian midline and the expansion or compression rate of the Donchian range, the indicator generates a forward propagation cone that reflects the prevailing trend and volatility state. This produces a cleaner, more analytically grounded projection of future price corridors, and it remains fully aligned with the signal precision of the underlying Donchian logic. █ How It Works ⚪ Donchian Core The script first computes a standard Donchian Channel over a configurable Length: Upper Band (dcHi) – highest high over the lookback. Lower Band (dcLo) – lowest low over the lookback. Midline (dcMd) – simple midpoint of upper and lower: (dcHi + dcLo)/ 2. f_getDonchian(length) => hi = ta.highest(high, length) lo = ta.lowest(low, length) md = (hi + lo) * 0.5 = f_getDonchian(lenDC) ⚪ Slope Estimation & Range Dynamics To turn the Donchian Channel into a predictive model, the script measures how both the midline and the range are changing over time: Midline Slope (mSl) – derived from a 1-bar difference in linear regression of the midline. Range Slope (rSl) – derived from a 1-bar difference in linear regression of the Donchian range (dcHi − dcLo). This pair describes both directional drift (uptrend vs. downtrend) and range expansion/compression (volatility regime). f_getSlopes(midLine, rngVal, length) => mSl = ta.linreg(midLine, length, 0) - ta.linreg(midLine, length, 1) rSl = ta.linreg(rngVal, length, 0) - ta.linreg(rngVal, length, 1) ⚪ Forward Projection Engine At the last bar, the indicator constructs a set of forward points for the mid, upper, and lower projections over Forecast Bars: The midline is projected linearly using the midline slope per bar. The range is adjusted using the range slope per bar, creating either a widening cone (expansion) or a tightening cone (compression). Upper and lower projections are then anchored around the projected midline, with logic that keeps the structure consistent and prevents pathological flips when slope changes sign. f_generatePoints(hi0, md0, lo0, steps, midSlp, rngSlp) => upPts = array.new() mdPts = array.new() dnPts = array.new() fillPts = array.new() hi_vals = array.new_float() md_vals = array.new_float() lo_vals = array.new_float() curHiLocal = hi0 curLoLocal = lo0 curMidLocal = md0 segBars = math.floor(steps / 3) segBars := segBars < 1 ? 1 : segBars for b = 0 to steps mdProj = md0 + midSlp * b prevRange = curHiLocal - curLoLocal rngProj = prevRange + rngSlp * b hiTemp = 0.0 loTemp = 0.0 if midSlp >= 0 hiTemp := math.max(curHiLocal, mdProj + rngProj * 0.5) loTemp := math.max(curLoLocal, mdProj - rngProj * 0.5) else hiTemp := math.min(curHiLocal, mdProj + rngProj * 0.5) loTemp := math.min(curLoLocal, mdProj - rngProj * 0.5) hiProj = hiTemp < mdProj ? curHiLocal : hiTemp loProj = loTemp > mdProj ? curLoLocal : loTemp if b % segBars == 0 curHiLocal := hiProj curLoLocal := loProj curMidLocal := mdProj array.push(hi_vals, curHiLocal) array.push(md_vals, curMidLocal) array.push(lo_vals, curLoLocal) array.push(upPts, chart.point.from_index(bar_index + b, curHiLocal)) array.push(mdPts, chart.point.from_index(bar_index + b, curMidLocal)) array.push(dnPts, chart.point.from_index(bar_index + b, curLoLocal)) ptSet.new(upPts, mdPts, dnPts) ⚪ Rejection Signals The script also tracks failed Donchian breakouts and marks them as potential reversal/reversion cues: Signal Down: Triggered when price makes an attempt above the upper Donchian band but then pulls back inside and closes above the midline, provided enough bars have passed since the last signal. Signal Up: Triggered when price makes an attempt below the lower Donchian band but then snaps back inside and closes below the midline, also requiring sufficient spacing from the previous signal. // Base signal conditions (unfiltered) bearCond = high < dcHi and high >= dcHi and close > dcMd and bar_index - lastMarker >= lenDC bullCond = low > dcLo and low <= dcLo and close < dcMd and bar_index - lastMarker >= lenDC // Apply MA filter if enabled if signalfilter bearCond := bearCond and close < ma // Bearish only below MA bullCond := bullCond and close > ma // Bullish only above MA signalUp := false signalDn := false if bearCond lastMarker := bar_index signalDn := true if bullCond lastMarker := bar_index signalUp := true █ How to Use The Donchian Predictive Channel is designed to outline possible future price trajectories. Treat it as a directional guide, not a fixed prediction tool. ⚪ Map Future Support & Resistance Use the projected upper and lower paths as dynamic future reference levels: Projected upper band ≈ is likely a resistance corridor if the current trend and volatility persist. Projected lower band ≈ likely support corridor or expected downside range. ⚪ Trend Path & Volatility Cone Because the projection is driven by midline and range slopes, the channel behaves like a trend + volatility cone: Steep positive midline slope + expanding range → accelerating, high-volatility trend. Flat midline + compressing range → coiling/contracting regime ahead of potential expansion. This helps you distinguish between a gentle drift and an aggressive move that likely needs more risk buffer. ⚪ Reversion & Rejection Signals The Donchian-based signals are especially useful for mean-reversion and fade-style trades. A Signal Down near the upper band can mark a failed breakout and a potential rotation back toward the midline or the lower projected band. A Signal Up near the lower band can flag a failed breakdown and a potential snap-back up the channel. When Filter Signals is enabled, these signals are only generated when they align with the chart’s directional bias as defined by the moving average. Bullish signals are allowed only when the price is above the MA, and bearish signals only when the price is below it. This reduces noise and helps ensure that reversions occur in harmony with the prevailing trend environment. █ Settings Length – Donchian lookback length. Higher values produce a smoother channel with fewer but more stable signals. Lower values make the channel more reactive and increase sensitivity at the cost of more noise. Forecast Bars – Number of bars used for projecting the Donchian channel forward. Higher values create a broader, longer-term projection. Lower values focus on short-horizon price path scenarios. Filter Signals – Enables directional filtering of Donchian signals using the selected moving average. When ON, bullish signals only trigger when the price is above the MA, and bearish signals only trigger when the price is below it. This helps reduce noise and aligns reversions with the broader trend context. Moving Average Type – The type of moving average used for signal filtering and optional plotting. Choose between SMA, EMA, WMA, or HMA depending on desired responsiveness. Faster averages (EMA, HMA) react quickly, while slower ones (SMA, WMA) smooth out short-term noise. Moving Average Length – Lookback length of the moving average. Higher values create a slower, more stable trend filter. Lower values track price more tightly and can flip the directional bias more frequently. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

Indicateur Pine Script®

MOMO Exhaustion Short Signal Strategy v6 alexh1166 Prints Short Signals for Exhausted Momentum stocks primed for reversals

Indicateur Pine Script®

Relative Performance Areas [LuxAlgo]The Relative Performance Areas tool enables traders to analyze the relative performance of any asset against a user-selected benchmark directly on the chart, session by session. The tool features three display modes for rescaled benchmark prices, as well as a statistics panel providing relevant information about overperforming and underperforming streaks. 🔶 USAGE Usage is straightforward. Each session is highlighted with an area displaying the asset price range. By default, a green background is displayed when the asset outperforms the benchmark for the session. A red background is displayed if the asset underperforms the benchmark. The benchmark is displayed as a green or red line. An extended price area is displayed when the benchmark exceeds the asset price and is set to SPX by default, but traders can choose any ticker from the settings panel. Using benchmarks to compare performance is a common practice in trading and investing. Using indexes such as the S&P 500 (SPX) or the NASDAQ 100 (NDX) to measure our portfolio's performance provides a clear indication of whether our returns are above or below the broad market. As the previous chart shows, if we have a long position in the NASDAQ 100 and buy an ETF like QQQ, we can clearly see how this position performs against BTSUSD and GOLD in each session. Over the last 15 sessions, the NASDAQ 100 outperformed the BTSUSD in eight sessions and the GOLD in six sessions. Conversely, it underperformed the BTCUSD in seven sessions and the GOLD in nine sessions. 🔹 Display Mode The display mode options in the Settings panel determine how benchmark performance is calculated. There are three display modes for the benchmark: Net Returns: Uses the raw net returns of the benchmark from the start of the session. Rescaled Returns: Uses the benchmark net returns multiplied by the ratio of the benchmark net returns standard deviation to the asset net returns standard deviation. Standardized Returns: Uses the z-score of the benchmark returns multiplied by the standard deviation of the asset returns. Comparing net returns between an asset and a benchmark provides traders with a broad view of relative performance and is straightforward. When traders want a better comparison, they can use rescaled returns. This option scales the benchmark performance using the asset's volatility, providing a fairer comparison. Standardized returns are the most sophisticated approach. They calculate the z-score of the benchmark returns to determine how many standard deviations they are from the mean. Then, they scale that number using the asset volatility, which is measured by the asset returns standard deviation. As the chart above shows, different display modes produce different results. All of these methods are useful for making comparisons and accounting for different factors. 🔹 Dashboard The statistics dashboard is a great addition that allows traders to gain a deep understanding of the relationship between assets and benchmarks. First, we have raw data on overperforming and underperforming sessions. This shows how many sessions the asset performance at the end of the session was above or below the benchmark. Next, we have the streaks statistics. We define a streak as two or more consecutive sessions where the asset overperformed or underperformed the benchmark. Here, we have the number of winning and losing streaks (winning means overperforming and losing means underperforming), the median duration of each streak in sessions, the mode (the number of sessions that occurs most frequently), and the percentages of streaks with durations equal to or greater than three, four, five, and six sessions. As the image shows, these statistics are useful for traders to better understand the relative behavior of different assets. 🔶 SETTINGS Benchmark: Benchmark for comparison Display Mode: Choose how to display the benchmark; Net Returns: Uses the raw net returns of the benchmark. Rescaled Returns: Uses the benchmark net returns multiplied by the ratio of the benchmark and asset standard deviations. Standardized Returns: Uses the benchmark z-score multiplied by the asset standard deviation. 🔹 Dashboard Dashboard: Enable or disable the dashboard. Position: Select the location of the dashboard. Size: Select the dashboard size. 🔹 Style Overperforming: Enable or disable displaying overperforming sessions and choose a color. Underperforming: Enable or disable displaying underperforming sessions and choose a color. Benchmark: Enable or disable displaying the benchmark and choose colors.

Indicateur Pine Script®

Bitcoin vs M2 Global Liquidity (Lead 3M) - Table Ticker ═══════════════════════════════════════════════════════════════ Bitcoin vs M2 Global Liquidity - Regression Indicator ═══════════════════════════════════════════════════════════════ TECHNICAL SPECS • Pine Script v6 • Overlay: false (separate pane) • Data sources: 5 M2 series + 4 FX pairs (request.security) • Calculation: Rolling OLS linear regression with configurable lead • Output: Regression line + ±1σ/±2σ confidence bands + R² ticker CORE FUNCTIONALITY Aggregates M2 money supply from 5 central banks (CN, US, EU, JP, GB), converts to USD, applies time-lead, runs rolling linear regression vs Bitcoin price, plots predicted value with confidence intervals. CONFIGURABLE PARAMETERS Input Controls: • Lead Period: 0-365 days (default: 90) • Lookback Window: 50-2000 bars (default: 750) • Bands: Toggle ±1σ and ±2σ visibility • Colors: BTC, M2, regression line, confidence zones • Ticker: Position, size, colors, transparency Advanced Settings: • Table display: R², lead, M2 total, country breakdown (%) • Ticker customization: 9 position options, 6 text sizes • Border: Width 0-10px, color, outline-only mode DATA AGGREGATION Sources (via request.security): • ECONOMICS:CNM2, USM2, EUM2, JPM2, GBM2 • FX_IDC:CNYUSD, JPYUSD (others: FX:EURUSD, GBPUSD) • Conversion: All M2 → USD → Sum / 1e12 (trillions) REGRESSION ENGINE • Arrays: m2Array, btcArray (dynamic sizing, auto-trim) • Window: Rolling (lookbackPeriod bars) • Lead: Time-shift via array indexing (i + leadPeriodDays) • Calc: Manual OLS (covariance/variance), no built-in ta functions • Outputs: slope, intercept, r2, stdResiduals CONFIDENCE BANDS ±1σ and ±2σ calculated from standard deviation of residuals. Fill zones between upper/lower bounds with configurable transparency. ALERTS 5 pre-configured alertcondition(): • Divergence > 15% • Price crosses ±1σ bands (up/down) • Price crosses ±2σ bands (up/down) TICKER TABLE Dynamic table.new() with 9 rows: • R² value (4 decimals) • Lead period (days + months) • M2 Global total (trillions USD) • Country breakdown: CN, US, EU, JP, GB (absolute + %) • Optional: Hide/show M2 details VISUAL CUSTOMIZATION All plot() elements support: • Color picker inputs (group="Couleurs") • Line width: 1-3px • Transparency: 0-100% for zones • Offset: M2 plot has +leadPeriodDays offset option PERFORMANCE • Max arrays size: lookbackPeriod + leadPeriodDays + 200 • Calculations: Only when array.size >= lookbackPeriod + leadPeriodDays • Table update: barstate.islast (once per bar) • Request.security: gaps_off mode CODE STRUCTURE 1. Inputs (lines 7-54) 2. Data fetch (lines 56-76) 3. M2 aggregation (line 78) 4. Array management (lines 84-95) 5. Regression calc (lines 97-172) 6. Prediction + bands (lines 174-183) 7. Plots (lines 185-199) 8. Ticker table (lines 201-236) 9. Alerts (lines 238-246) DEPENDENCIES None. Pure Pine Script v6. No external libraries. LIMITATIONS • Daily timeframe recommended (1D) • Requires 750+ bars history for optimal calculation • M2 data availability: TradingView ECONOMICS feed • Max lines: 500 (declared in indicator()) CUSTOMIZATION EXAMPLES • Shorter lookback (200d): More reactive, lower R² • Longer lookback (1500d): More stable, regime mixing • No bands: Set showBands=false for clean view • Different lead: Test 60d, 120d for sensitivity analysis TECHNICAL NOTES • Manual OLS implementation (no ta.linreg) • Array-based lead application (not plot offset) • M2 values stored in trillions (/ 1e12) for readability • Residuals array cleared/rebuilt each calculation OPEN SOURCE Code fully visible. Modify, fork, analyze freely. No hidden calculations. No proprietary data. VERSION 1.0 | November 2025 | Pine Script v6 ═══════════════════════════════════════════════════════════════

Indicateur Pine Script®

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Smart Trend Signal with Bands [wjdtks255]Indicator Description for TradingView Title: Adaptive Trend Kernel Description: The "Adaptive Trend Kernel " is a versatile trend-following and volatility indicator designed to help traders identify dynamic market trends, potential reversals, and price extremes within a channel. Built upon a customized linear regression model, this indicator provides clear visual cues to enhance your trading decisions. Key Features: Regression Line: A central dynamic line representing the core trend direction, calculated based on a user-defined "Regression Length." Regression Bands: Standard deviation-based bands plotted around the Regression Line, which act like a dynamic channel. These bands expand and contract with market volatility, indicating potential overbought/oversold conditions relative to the trend. Trend Reversal Signals: Distinct "Up" (green triangle up) and "Down" (red triangle down) signals are generated when the price (close) crosses over or under the Regression Line. These signals suggest potential shifts in the short-term trend direction. Visual Customization: Highly flexible input options for adjusting line colors, band colors, line width, and panel opacity. Users can toggle the visibility of bands and trend labels to suit their chart preferences. Panel Label: A subtle "Regression" label is dynamically positioned, offering clear context without cluttering the main chart. How it Works: The indicator calculates a linear regression line as the adaptive center of the price movement. Standard deviation is then used to create upper and lower bands, encapsulating typical price fluctuations. Signals are fired when price breaks out of the regression line, suggesting a momentum shift in line with the established trend or a potential reversal. Trading Methods & Strategies Here are some trading strategies you can apply using the "Adaptive Trend Kernel " indicator: Trend-Following with Confirmation: Long Entry: Look for an "Up" signal (green triangle up) when the price is above the Regression Line, especially after a brief retracement towards the line. This confirms that the uptrend is likely resuming. Short Entry: Look for a "Down" signal (red triangle down) when the price is below the Regression Line, especially after a brief rally towards the line. This confirms that the downtrend is likely resuming. Exit Strategy: Consider exiting if an opposite signal appears, or if the price closes outside the opposite band, indicating potential overextension or reversal. Reversal / Counter-Trend Play: Long Entry (Aggressive): When the price approaches or briefly dips below the Lower Regression Band and then generates an "Up" signal (green triangle up). This could indicate a potential bounce from an oversold condition relative to the trend. Short Entry (Aggressive): When the price approaches or briefly moves above the Upper Regression Band and then generates a "Down" signal (red triangle down). This could indicate a potential pullback from an overbought condition relative to the trend. Confirmation: This strategy works best when combined with other reversal confirmation patterns (e.g., bullish/bearish engulfing candlesticks) or divergences in other momentum indicators (like RSI). Volatility Breakout: Entry (Long): After a period of low volatility where the Regression Bands are narrow, observe if the price decisively breaks above the Upper Regression Band and an "Up" signal appears. This suggests a strong bullish momentum breakout. Entry (Short): After a period of low volatility where the Regression Bands are narrow, observe if the price decisively breaks below the Lower Regression Band and a "Down" signal appears. This suggests a strong bearish momentum breakdown. Management: Volatility breakouts can be swift; use appropriate risk management and profit-taking strategies. Important Considerations: Risk Management: Always apply proper stop-loss and take-profit levels. No indicator is infallible. Timeframe Sensitivity: Adjust the "Regression Length" and "Band Multiplier" according to the asset and timeframe you are trading. Shorter lengths might suit scalping, while longer lengths are better for swing trading. Confirmation with Other Tools: For higher conviction trades, use this indicator in conjunction with other technical analysis tools such like volume, MACD, or RSI on an oscillator pane. Backtesting: Always backtest any strategy on historical data to understand its performance characteristics before live trading.

Indicateur Pine Script®

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Données de marché sélectionnées fournies par ICE Data Services. Données de référence sélectionnées fournies par FactSet. Copyright © 2026 FactSet Research Systems Inc.Copyright © 2026, American Bankers Association. Base de données CUSIP fournie par FactSet Research Systems Inc. Tous droits réservés. Documents déposés auprès de la SEC et autres documents fournis par Quartr.© 2026 TradingView, Inc.

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