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Candle Numbers (last N, no bubble) Candle Numbers (last N, no bubble) is a lightweight utility indicator that labels candles with sequential numbers to make chart analysis and discussion easier (e.g., “candle 213”, “the breakout candle”, “the pivot”). It is designed for clarity and performance: labels are text-only (no background bubble) and are drawn only for the last N bars. What it does Numbers the last N candles on the chart (a sliding window near the most recent bar). Counting starts at the left edge of that window: the leftmost bar in the window is 1 the most recent bar in the window is N (or fewer if you use stepping / limits). Allows numbering every Nth bar to keep the chart clean. Places numbers below each candle, with a configurable vertical offset measured in ticks. Inputs Bars to number (last N) (barsWindow) Size of the numbered window (default 200). Number every N bars (step) 1 = every bar, 2 = every second bar, 5 = every fifth bar, etc. Text color (txtColor) Text size (txtSizeIn) tiny / small / normal / large Vertical offset (ticks) (offsetTick) Moves the label down by offsetTick * syminfo.mintick. You can use large values if needed. Max numbers to plot (maxMarks) Extra safeguard to control label count and performance. How it works (implementation notes) Labels are drawn only when barstate.islast is true (updates on the latest bar). Previously created labels are deleted and re-created each update to avoid clutter. Uses max_labels_count=500 plus maxMarks to stay within TradingView label limits. Notes This is not a trading signal indicator. It’s a chart annotation tool for analysis and manual backtesting.
Chỉ báo Pine Script®
của Walerjan
MTT US Economic Health Z-ScoreTo use the US Economic Health Z-Score, you must set your TradingView chart to a Monthly (M) timeframe. This is critical because the script aggregates high-level data from FRED—such as Manufacturing PMI and Building Permits—which are released on a monthly cycle. Viewing this on a Daily or Intraday chart will result in flat, "stair-step" lines that obscure the true momentum of the data. How to Interpret the Data The indicator functions as a normalized macro-filter, converting five distinct economic sectors into a single standard deviation scale. The 0 Line: Represents the "historical norm." Individual Colored Lines: Track specific sectors (e.g., Sentiment, Labor, Housing). The Composite Line (White): This is your aggregate health signal. Signal Logic Economic struggle is identified when the Composite Score trends below -1.0 (At Risk) or drops past -1.5 (Struggling). Because these are leading indicators, they often deteriorate months before the stock market reflects the damage. Use this dashboard to identify bearish divergence: if the S&P 500 is rising while the US Economic Health Z-Score is falling, the market is likely ignoring fundamental cracks. This tool is designed to help you shift toward a defensive portfolio posture before the "lagging" data (like the unemployment rate) confirms the downturn.
Chỉ báo Pine Script®
của mad_tom_trading
Seasonality (Prev Month Close Expected)Seasonality Indicator This indicator shows how an asset has historically behaved during each calendar month. It highlights the typical price direction and strength for the current month based on long-term seasonal patterns. The projected zone on the chart represents the average historical outcome for the ongoing month, allowing traders to quickly see whether current price action is developing in line with, above, or below its usual seasonal behavior. A heatmap summarizes monthly performance across years, making recurring strong and weak periods easy to identify. Vladimir Popdimitrov
Chỉ báo Pine Script®
của Market_Atlas
Cross-Market Regime Scanner [BOSWaves]Cross-Market Regime Scanner - Multi-Asset ADX Positioning with Correlation Network Visualization Overview Cross-Market Regime Scanner is a multi-asset regime monitoring system that maps directional strength and trend intensity across correlated instruments through ADX-based coordinate positioning, where asset locations dynamically reflect their current trending versus ranging state and bullish versus bearish bias. Instead of relying on isolated single-asset trend analysis or static correlation matrices, regime classification, spatial positioning, and intermarket relationship strength are determined through ADX directional movement calculation, percentile-normalized coordinate mapping, and rolling correlation network construction. This creates dynamic regime boundaries that reflect actual cross-market momentum patterns rather than arbitrary single-instrument levels - visualizing trending assets in right quadrants when ADX strength exceeds thresholds, positioning ranging assets in left quadrants during consolidation, and incorporating correlation web topology to reveal which instruments move together or diverge during regime transitions. Assets are therefore evaluated relative to ADX-derived regime coordinates and correlation network position rather than conventional isolated technical indicators. Conceptual Framework Cross-Market Regime Scanner is founded on the principle that meaningful market insights emerge from simultaneous multi-asset regime awareness rather than sequential single-instrument analysis. Traditional trend analysis examines assets individually using separate chart windows, which often obscures the broader cross-market regime structure and correlation patterns that drive coordinated moves. This framework replaces isolated-instrument logic with unified spatial positioning informed by actual ADX directional measurements and correlation relationships. Three core principles guide the design: Asset positioning should be determined by ADX-based regime coordinates that reflect trending versus ranging state and directional bias simultaneously. Spatial mapping must normalize ADX values to place assets within consistent quadrant boundaries regardless of instrument volatility characteristics. Correlation network visualization reveals which assets exhibit coordinated behavior versus divergent regime patterns during market transitions. This shifts regime analysis from isolated single-chart monitoring into unified multi-asset spatial awareness with correlation context. Theoretical Foundation The indicator combines ADX directional movement calculation, coordinate normalization methodology, quadrant-based regime classification, and rolling correlation network construction. A Wilder's smoothing implementation calculates ADX, +DI, and -DI for each monitored asset using True Range and directional movement components. The ADX value relative to a configurable threshold determines X-axis positioning (ranging versus trending), while the difference between +DI and -DI determines Y-axis positioning (bearish versus bullish). Coordinate normalization caps values within fixed boundaries for consistent quadrant placement. Pairwise correlation calculations over rolling windows populate a network graph where line thickness and opacity reflect correlation strength. Five internal systems operate in tandem: Multi-Asset ADX Engine : Computes smoothed ADX, +DI, and -DI values for up to 8 configurable instruments using Wilder's directional movement methodology. Coordinate Transformation System : Converts ADX strength and directional movement into normalized X/Y coordinates with threshold-relative scaling and boundary capping. Quadrant Classification Logic : Maps coordinate positions to four distinct regime states—Trending Bullish, Trending Bearish, Ranging Bullish, Ranging Bearish—with color-coded zones. Historical Trail Rendering : Maintains rolling position history for each asset, drawing gradient-faded trails that visualize recent regime trajectory and velocity. Correlation Network Calculator : Computes pairwise return correlations across all enabled assets, rendering weighted connection lines in circular web topology with strength-based styling. This design allows simultaneous cross-market regime awareness rather than reacting sequentially to individual instrument signals. How It Works Cross-Market Regime Scanner evaluates markets through a sequence of multi-asset spatial processes: Data Request Processing : Security function retrieves high, low, and close values for up to 8 configurable symbols with lookahead offset to ensure confirmed bar data. ADX Calculation Per Asset : True Range computed from high-low-close relationships, directional movement derived from up-moves versus down-moves, smoothed via Wilder's method over configurable period. Directional Index Derivation : +DI and -DI calculated as smoothed directional movement divided by smoothed True Range, scaled to percentage values. Coordinate Transformation : X-axis position equals (ADX - threshold) * 2, capped between -50 and +50; Y-axis position equals (+DI - -DI), capped between -50 and +50. Quadrant Assignment : Positive X indicates trending (ADX > threshold), negative X indicates ranging; positive Y indicates bullish (+DI > -DI), negative Y indicates bearish. Trail History Management : Configurable-length position history maintains recent coordinates for each asset, rendering gradient-faded lines connecting sequential positions. Velocity Vector Calculation : 7-bar coordinate change converted to directional arrow overlays showing regime momentum and trajectory. Return Correlation Processing : Bar-over-bar returns calculated for each asset, pairwise correlations computed over rolling window. Network Graph Construction : Assets positioned in circular topology, correlation lines drawn between pairs exceeding threshold with thickness/opacity scaled by correlation strength, positive correlations solid green, negative correlations dashed red. Risk Regime Scoring : Composite score aggregates bullish risk-on assets (equities, crypto, commodities) minus bullish risk-off assets (gold, dollar, VIX), generating overall market risk sentiment with colored candle overlay. Together, these elements form a continuously updating spatial regime framework anchored in multi-asset momentum reality and correlation structure. Interpretation Cross-Market Regime Scanner should be interpreted as unified spatial regime boundaries with correlation context: Top-Right Quadrant (TREND ▲) : Assets positioned here exhibit ADX above threshold with +DI exceeding -DI - confirmed bullish trending conditions with directional conviction. Bottom-Right Quadrant (TREND ▼) : Assets positioned here exhibit ADX above threshold with -DI exceeding +DI - confirmed bearish trending conditions with directional conviction. Top-Left Quadrant (RANGE ▲) : Assets positioned here exhibit ADX below threshold with +DI exceeding -DI - ranging consolidation with bullish bias but insufficient trend strength. Bottom-Left Quadrant (RANGE ▼) : Assets positioned here exhibit ADX below threshold with -DI exceeding +DI - ranging consolidation with bearish bias but insufficient trend strength. Position Trails : Gradient-faded lines connecting recent coordinate history reveal regime trajectory - curved paths indicate regime rotation, straight paths indicate sustained directional conviction. Velocity Arrows : Directional vectors overlaid on current positions show 7-bar regime momentum - arrow length indicates speed of regime change, angle indicates trajectory direction. Correlation Web : Circular network graph positioned left of main quadrant map displays pairwise asset relationships - solid green lines indicate positive correlation (moving together), dashed red lines indicate negative correlation (diverging moves), line thickness reflects correlation strength magnitude. Asset Dots : Multi-layer glow effects with color-coded markers identify each asset on both quadrant map and correlation web-symbol labels positioned adjacent to current location. Regime Summary Bar : Vertical boxes on right edge display condensed regime state for each enabled asset - box background color reflects quadrant classification, border color matches asset identifier. Risk Regime Candles : Overlay candles on price chart colored by composite risk score - green indicates risk-on dominance (bullish equities/crypto exceeding bullish safe-havens), red indicates risk-off dominance (bullish gold/dollar/VIX exceeding bullish risk assets), gray indicates neutral balance. Quadrant positioning, trail trajectory, correlation network topology, and velocity vectors outweigh isolated single-asset readings. Signal Logic & Visual Cues Cross-Market Regime Scanner presents spatial positioning insights rather than discrete entry signals: Regime Clustering : Multiple assets congregating in same quadrant suggests broad market regime consensus - all assets in TREND ▲ indicates coordinated bullish momentum across instruments. Regime Divergence : Assets splitting across opposing quadrants reveals intermarket disagreement - equities in TREND ▲ while safe-havens in TREND ▼ suggests healthy risk-on environment. Quadrant Transitions : Assets crossing quadrant boundaries mark regime shifts - movement from left (ranging) to right (trending) indicates breakout from consolidation into directional phase. Trail Curvature Patterns : Sharp curves in position trails signal rapid regime rotation, straight trails indicate sustained directional conviction, loops indicate regime uncertainty with back-and-forth oscillation. Velocity Acceleration : Long arrows indicate rapid regime change momentum, short arrows indicate stable regime persistence, arrow direction reveals whether asset moving toward trending or ranging state. Correlation Breakdown Events : Previously strong correlation lines (thick, opaque) suddenly thinning or disappearing indicates relationship decoupling - often precedes major regime transitions. Correlation Inversion Signals : Assets shifting from positive correlation (solid green) to negative correlation (dashed red) marks structural market regime change - historically correlated assets beginning to diverge. Risk Score Extremes : Composite score reaching maximum positive (all risk-on bullish, all risk-off bearish) or maximum negative (all risk-on bearish, all risk-off bullish) marks regime conviction extremes. The primary value lies in simultaneous multi-asset regime awareness and correlation pattern recognition rather than isolated timing signals. Strategy Integration Cross-Market Regime Scanner fits within macro-aware and intermarket analysis approaches: Regime-Filtered Entries : Use quadrant positioning as directional filter for primary trading instrument - favor long setups when asset in TREND ▲ quadrant, short setups in TREND ▼ quadrant. Correlation Confluence Trading : Enter positions when target asset and correlated instruments occupy same quadrant - multiple assets in TREND ▲ provides conviction for long exposure. Divergence-Based Reversal Anticipation : Monitor for regime divergence between correlated assets - if historically aligned instruments split to opposite quadrants, anticipate mean-reversion or regime rotation. Breakout Confirmation via Cross-Asset Validation : Confirm primary instrument breakouts by verifying correlated assets simultaneously transitioning from ranging to trending quadrants. Risk-On/Risk-Off Positioning : Use composite risk score and safe-haven positioning to determine overall market environment - scale risk exposure based on risk regime dominance. Velocity-Based Timing : Enter during periods of high regime velocity (long arrows) when momentum carries assets decisively into new quadrants, avoid entries during low velocity regime uncertainty. Multi-Timeframe Regime Alignment : Apply higher-timeframe regime scanner to establish macro context, use lower-timeframe price action for entry timing within aligned regime structure. Correlation Web Pattern Recognition : Identify regime transitions early by monitoring correlation network topology changes - previously disconnected assets forming strong correlations suggests regime coalescence. Technical Implementation Details Core Engine : Wilder's smoothing-based ADX calculation with separate True Range and directional movement tracking per asset Coordinate Model : Threshold-relative X-axis scaling (trending versus ranging) with directional movement differential Y-axis (bullish versus bearish) Normalization System : Boundary capping at ±50 for consistent spatial positioning regardless of instrument volatility Trail Rendering : Rolling array-based position history with gradient alpha decay and width tapering Correlation Engine : Return-based pairwise correlation calculation over rolling window with configurable lookback Network Visualization : Circular topology with trigonometric positioning, weighted line rendering based on correlation magnitude Risk Scoring : Composite calculation aggregating directional states across classified risk-on and risk-off asset categories Performance Profile : Optimized for 8 simultaneous security requests with efficient array management and conditional rendering Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Micro-regime monitoring for intraday correlation shifts and short-term regime rotations 15 - 60 min : Intraday regime structure with meaningful ADX development and correlation stability 4H - Daily : Swing and position-level macro regime identification with sustained trend classification Weekly - Monthly : Long-term regime cycle tracking with structural correlation pattern evolution Suggested Baseline Configuration: ADX Period : 14 ADX Smoothing : 14 Trend Threshold : 25.0 Trail Length : 15 Correlation Period : 50 Min |Correlation| to Show Line : 0.3 Web Radius : 30 Show Quadrant Colors : Enabled Show Regime Summary Bar : Enabled Show Velocity Arrows : Enabled Show Correlation Web : Enabled These suggested parameters should be used as a baseline; their effectiveness depends on the selected assets' volatility profiles, correlation characteristics, and preferred spatial sensitivity, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Assets clustering too tightly : Decrease Trend Threshold (e.g., 20) to spread ranging/trending separation, or increase ADX Period for smoother ADX calculation reducing noise. Assets spreading too widely : Increase Trend Threshold (e.g., 30-35) to demand stronger ADX confirmation before classifying as trending, tightening quadrant boundaries. Trail too short to show trajectory : Increase Trail Length (20-25) to visualize longer regime history, revealing sustained directional patterns. Trail too cluttered : Decrease Trail Length (8-12) for cleaner visualization focusing on recent regime state, reducing visual complexity. Unstable ADX readings : Increase ADX Period and ADX Smoothing (18-21) for heavier smoothing reducing bar-to-bar regime oscillation. Sluggish regime detection : Decrease ADX Period (10-12) for faster response to directional changes, accepting increased sensitivity to noise. Too many correlation lines : Increase Min |Correlation| threshold (0.4-0.6) to display only strongest relationships, decluttering network visualization. Missing significant correlations : Decrease Min |Correlation| threshold (0.2-0.25) to reveal weaker but potentially meaningful relationships. Correlation too volatile : Increase Correlation Period (75-100) for more stable correlation measurements, reducing network line flickering. Correlation too stale : Decrease Correlation Period (30-40) to emphasize recent correlation patterns, capturing regime-dependent relationship changes. Velocity arrows too sensitive : Modify 7-bar lookback in code to longer period (10-14) for smoother velocity representation, or increase magnitude threshold for arrow display. Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions. Performance Characteristics High Effectiveness: Macro-aware trading approaches requiring cross-market regime context for directional bias Intermarket analysis strategies monitoring correlation breakdowns and regime divergences Portfolio construction decisions requiring simultaneous multi-asset regime classification Risk management frameworks using safe-haven positioning and risk-on/risk-off scoring Trend-following systems benefiting from cross-asset regime confirmation before entry Mean-reversion strategies identifying regime extremes via clustering patterns and correlation stress Reduced Effectiveness: Single-asset focused strategies not incorporating cross-market context in decision logic High-frequency trading approaches where multi-security request latency impacts execution Markets with consistently weak correlations where network topology provides limited insight Extremely low volatility environments where ADX remains persistently below threshold for all assets Instruments with erratic or unreliable ADX characteristics producing unstable coordinate positioning Integration Guidelines Confluence : Combine with BOSWaves structure, volume analysis, or primary instrument technical indicators for entry timing within aligned regime Quadrant Respect : Trust signals occurring when primary trading asset occupies appropriate quadrant for intended trade direction Correlation Context : Prioritize setups where target asset exhibits strong correlation with instruments in same regime quadrant Divergence Awareness : Monitor for safe-haven assets moving opposite to risk assets - regime divergence validates directional conviction Velocity Confirmation : Favor entries during periods of strong regime velocity indicating decisive momentum rather than regime oscillation Risk Score Alignment : Scale position sizing and exposure based on composite risk score - larger positions during clear risk-on/risk-off environments Trail Pattern Recognition : Use trail curvature to identify regime stability (straight) versus rotation (curved) versus uncertainty (looped) Multi-Timeframe Structure : Apply higher-timeframe regime scanner for macro filter, lower-timeframe for tactical positioning within established regime Disclaimer Cross-Market Regime Scanner is a professional-grade multi-asset regime visualization and correlation analysis tool. It uses ADX-based coordinate positioning and rolling correlation calculation but does not predict future regime transitions or guarantee relationship persistence. Results depend on selected assets' characteristics, parameter configuration, correlation stability, and disciplined interpretation. Security request timing may introduce minor latency in real-time data retrieval. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, fundamental macro awareness, and comprehensive risk management.
Chỉ báo Pine Script®
của BOSWaves
Gold Decisions [DayFunded]Gold Decisions 🎯 A multi-timeframe decision system designed specifically for XAUUSD (Gold) traders who want clarity, not noise. 🔍 What It Does This indicator helps you identify high-probability trade setups by checking 5 key conditions: 1️⃣ Direction — Weekly + Daily must agree (no fighting the trend!) 2️⃣ Breakout — Daily closes beyond a key H4 zone 3️⃣ Pullback — Price returns to the cleared level (no chasing!) 4️⃣ Structure — 15-minute confirms with a break of structure 5️⃣ Entry — Clean directional close = signal When all gates pass, you get a simple BUY or SELL label with confidence level (H/M/L). 📊 Features ✅ Clean, minimal chart labels (no spam!) ✅ Smart panel showing exactly what to watch for ✅ Win/Loss tracking to see historical performance ✅ H4 Supply/Demand zones auto-detected ✅ Asia session levels (Gold reacts to these!) ✅ Weekly/Daily high-low reference points ✅ Pullback target line for easy visual ⚠️ Important Notes This is an indicator, not an EA — it does NOT place trades Signals fire on confirmed bar close — no repainting Works best on 15m to 4H timeframes Designed for XAUUSD but may work on other pairs 🎁 Free to Use This script is completely free. If you find it helpful, a follow or comment is always appreciated! 📖 How to Use Add to your Gold chart (15m-4H recommended) Watch the panel for "WATCH FOR" guidance Wait for BUY/SELL signal Check confidence level (H = High, M = Medium, L = Low) Manage your own risk Not financial advice. Trade responsibly. ✌️
Chỉ báo Pine Script®
của DayFunded
ATR Volatility ChannelATR Volatility Channel This indicator plots adaptive upper and lower volatility bands using EMA-smoothed highs and lows, expanded by ATR. Unlike Bollinger Bands, it uses true range instead of standard deviation, so the bands expand smoothly and predictably with actual price volatility. It highlights dynamic support, resistance, and fair value, and can be used for ATR level bounces and trend structure analysis. Settings: EMA Length: Smooths the highs and lows to calculate the channel (default: 10) ATR Length: Period used for the Average True Range (default: 14) ATR Multiplier: Scales the channel width (default: 2) Show Upper / Lower / Median
Chỉ báo Pine Script®
của seeyeahslater
Swing IA Cockpit [v2]//@version=5 indicator("Swing IA Cockpit ", overlay=true, max_bars_back=500) // === INPUTS === mode = input.string("Pullback", title="Entry Mode", options= ) corrLen = input.int(60, "Correlation Window Length") scoreWeightBias = input.float(0.6, title="Weight: Bias", minval=0, maxval=1) scoreWeightTiming = 1.0 - scoreWeightBias // === INDICATEURS H1 === ema200_H1 = ta.ema(close, 200) ema50_H1 = ta.ema(close, 50) rsi_H1 = ta.rsi(close, 14) donchianHigh = ta.highest(high, 20) donchianLow = ta.lowest(low, 20) atr_H1 = ta.atr(14) avgATR_H1 = ta.sma(atr_H1, 50) body = math.abs(close - open) avgBody = ta.sma(body, 20) // === H4 / D1 === close_H4 = request.security(syminfo.tickerid, "240", close) ema200_H4 = request.security(syminfo.tickerid, "240", ta.ema(close, 200)) rsi_H4 = request.security(syminfo.tickerid, "240", ta.rsi(close, 14)) atr_H4 = request.security(syminfo.tickerid, "240", ta.atr(14)) avgATR_H4 = request.security(syminfo.tickerid, "240", ta.sma(ta.atr(14), 50)) close_D1 = request.security(syminfo.tickerid, "D", close) ema200_D1 = request.security(syminfo.tickerid, "D", ta.ema(close, 200)) // === CORRÉLATIONS === dxy = request.security("TVC:DXY", "60", close) spx = request.security("SP:SPX", "60", close) gold = request.security("OANDA:XAUUSD", "60", close) corrDXY = ta.correlation(close, dxy, corrLen) corrSPX = ta.correlation(close, spx, corrLen) corrGold = ta.correlation(close, gold, corrLen) // === LOGIQUE BIAIS === biasLong = close_D1 > ema200_D1 and close_H4 > ema200_H4 and rsi_H4 >= 55 biasShort = close_D1 < ema200_D1 and close_H4 < ema200_H4 and rsi_H4 <= 45 bias = biasLong ? "LONG" : biasShort ? "SHORT" : "NEUTRAL" // === LOGIQUE TIMING === isBreakoutLong = mode == "Breakout" and high > donchianHigh and close > ema200_H1 and rsi_H1 > 50 isBreakoutShort = mode == "Breakout" and low < donchianLow and close < ema200_H1 and rsi_H1 < 50 var float breakoutPrice = na var int breakoutBar = na if isBreakoutLong or isBreakoutShort breakoutPrice := close breakoutBar := bar_index validPullbackLong = mode == "Pullback" and not na(breakoutBar) and bar_index <= breakoutBar + 3 and close > ema50_H1 and low <= ema50_H1 validPullbackShort = mode == "Pullback" and not na(breakoutBar) and bar_index <= breakoutBar + 3 and close < ema50_H1 and high >= ema50_H1 timingLong = isBreakoutLong or validPullbackLong timingShort = isBreakoutShort or validPullbackShort // === SCORES === scoreTrend = (close_D1 > ema200_D1 ? 20 : 0) + (close_H4 > ema200_H4 ? 20 : 0) scoreMomentumBias = (rsi_H4 >= 55 or rsi_H4 <= 45) ? 20 : 10 scoreCorr = 0 scoreCorr += biasLong and corrDXY < 0 ? 10 : 0 scoreCorr += biasLong and corrSPX > 0 ? 10 : 0 scoreCorr += biasLong and corrGold >= 0 ? 10 : 0 scoreCorr += biasShort and corrDXY > 0 ? 10 : 0 scoreCorr += biasShort and corrSPX < 0 ? 10 : 0 scoreCorr += biasShort and corrGold <= 0 ? 10 : 0 scoreCorr := math.min(scoreCorr, 30) scoreVolBias = atr_H4 > avgATR_H4 ? 10 : 0 scoreBias = scoreTrend + scoreMomentumBias + scoreCorr + scoreVolBias scoreStruct = (timingLong or timingShort) ? 40 : 0 scoreMomentumTiming = rsi_H1 > 50 or rsi_H1 < 50 ? 25 : 10 scoreTrendH1 = (close > ema50_H1 and ema50_H1 > ema200_H1) or (close < ema50_H1 and ema50_H1 < ema200_H1) ? 20 : 10 scoreVolTiming = atr_H1 > avgATR_H1 ? 15 : 5 scoreTiming = scoreStruct + scoreMomentumTiming + scoreTrendH1 + scoreVolTiming scoreTotal = scoreBias * scoreWeightBias + scoreTiming * scoreWeightTiming scoreLong = biasLong ? scoreTotal : 0 scoreShort = biasShort ? scoreTotal : 0 delta = scoreLong - scoreShort scoreExtMomentum = (rsi_H4 > 55 ? 10 : 0) scoreExtVol = atr_H4 > avgATR_H4 ? 10 : 0 scoreExtStructure = body > avgBody ? 10 : 5 scoreExtCorr = (scoreCorr > 15 ? 10 : 5) scoreExtension = scoreExtMomentum + scoreExtVol + scoreExtStructure + scoreExtCorr // === VERDICT FINAL === verdict = "NO TRADE" verdict := bias == "NEUTRAL" or math.abs(delta) < 10 or scoreTotal < 70 ? "NO TRADE" : scoreTotal < 80 ? "WAIT" : scoreTotal >= 85 and math.abs(delta) >= 20 and scoreExtension >= 60 ? "TRADE A+" : "TRADE" // === TABLE COCKPIT === var table cockpit = table.new(position.top_right, 2, 9, border_width=1) if bar_index % 5 == 0 table.cell(cockpit, 0, 0, "Bias", bgcolor=color.gray) table.cell(cockpit, 1, 0, bias) table.cell(cockpit, 0, 1, "ScoreBias", bgcolor=color.gray) table.cell(cockpit, 1, 1, str.tostring(scoreBias)) table.cell(cockpit, 0, 2, "ScoreTiming", bgcolor=color.gray) table.cell(cockpit, 1, 2, str.tostring(scoreTiming)) table.cell(cockpit, 0, 3, "ScoreTotal", bgcolor=color.gray) table.cell(cockpit, 1, 3, str.tostring(scoreTotal)) table.cell(cockpit, 0, 4, "ScoreLong", bgcolor=color.gray) table.cell(cockpit, 1, 4, str.tostring(scoreLong)) table.cell(cockpit, 0, 5, "ScoreShort", bgcolor=color.gray) table.cell(cockpit, 1, 5, str.tostring(scoreShort)) table.cell(cockpit, 0, 6, "Delta", bgcolor=color.gray) table.cell(cockpit, 1, 6, str.tostring(delta)) table.cell(cockpit, 0, 7, "Extension", bgcolor=color.gray) table.cell(cockpit, 1, 7, str.tostring(scoreExtension)) table.cell(cockpit, 0, 8, "Verdict", bgcolor=color.gray) table.cell(cockpit, 1, 8, verdict, bgcolor=verdict == "TRADE A+" ? color.green : verdict == "TRADE" ? color.lime : verdict == "WAIT" ? color.orange : color.red) // === ALERTS === alertcondition(verdict == "TRADE A+" and bias == "LONG", title="TRADE A+ LONG", message="TRADE A+ signal long") alertcondition(verdict == "TRADE A+" and bias == "SHORT", title="TRADE A+ SHORT", message="TRADE A+ signal short") alertcondition(verdict == "NO TRADE", title="NO TRADE / RANGE", message="Marché confus ou neutre — pas de trade")
Chỉ báo Pine Script®
của matpi2278
Volume Profile Skew [BackQuant]Volume Profile Skew Overview Volume Profile Skew is a market-structure indicator that answers a specific question most volume profiles do not: “Is volume concentrating toward lower prices (accumulation) or higher prices (distribution) inside the current profile range?” A standard volume profile shows where volume traded, but it does not quantify the shape of that distribution in a single number. This script builds a volume profile over a rolling lookback window, extracts the key profile levels (POC, VAH, VAL, and a volume-weighted mean), then computes the skewness of the volume distribution across price bins. That skewness becomes an oscillator, smoothed into a regime signal and paired with visual profile plotting, key level lines, and historical POC tracking. This gives you two layers at once: A full profile and its important levels (where volume is). A skew metric (how volume is leaning within that range). What this indicator is based on The foundation comes from classical “volume at price” concepts used in Market Profile and Volume Profile analysis: POC (Point of Control): the price level with the highest traded volume. Value Area (VAH/VAL): the zone containing the bulk of activity, commonly 70% of total volume. Volume-weighted mean (VWMP in this script): the average price weighted by volume, a “center of mass” for traded activity. Where this indicator extends the idea is by treating the volume profile as a statistical distribution across price. Once you treat “volume by price bin” as a probability distribution (weights sum to 1), you can compute distribution moments: Mean: where the mass is centered. Standard deviation: how spread-out it is. Skewness: whether the distribution has a heavier tail toward higher or lower prices. This is not a gimmick. Skewness is a standard statistic in probability theory. Here it is applied to “volume concentration across price”, not to returns. Core concept: what “skew” means in a volume profile Imagine a profile range from Low to High, split into bins. Each bin has some volume. You can get these shapes: Balanced profile: volume is fairly symmetric around the mean, skew near 0. Bottom-heavy profile: more volume at lower prices, with a tail toward higher prices, skew tends to be positive. Top-heavy profile: more volume at higher prices, with a tail toward lower prices, skew tends to be negative. In this script: Positive skew is labeled as ACCUMULATION. Negative skew is labeled as DISTRIBUTION. Near-zero skew is NEUTRAL. Important: accumulation here does not mean “buying will immediately pump price.” It means the profile shape suggests more participation at lower prices inside the current lookback range. Distribution means participation is heavier at higher prices. How the volume profile is built 1) Define the analysis window The profile is computed on a rolling window: Lookback Period: number of bars included (capped by available history). Profile Resolution (bins): number of price bins used to discretize the high-low range. The script finds the highest high and lowest low in the lookback window to define the price range: rangeHigh = highest high in window rangeLow = lowest low in window binSize = (rangeHigh - rangeLow) / bins 2) Create bin midpoints Each bin gets a midpoint “price” used for calculations: price = rangeLow + binSize * (b + 0.5) These midpoints are what the mean, variance, and skewness are computed on. 3) Distribute each candle’s volume into bins This is a key implementation detail. Real volume profiles require tick-level data, but Pine does not provide that. So the script approximates volume-at-price using candle ranges: For each bar in the lookback: Determine which bins its low-to-high range touches. Split that candle’s total volume evenly across the touched bins. So if a candle spans 6 bins, each bin gets volume/6 from that bar. This is a practical, consistent approximation for “where trading could have occurred” inside the bar. This approach has tradeoffs: It does not know where within the candle the volume truly traded. It assumes uniform distribution across the candle range. It becomes more meaningful with larger samples (bigger lookback) and/or higher timeframes. But it is still useful because the purpose here is the shape of the distribution across the whole window, not exact microstructure. Key profile levels: POC, VAH, VAL, VWMP POC (Point of Control) POC is found by scanning bins and selecting the bin with maximum volume. The script stores: pocIndex: which bin has max volume poc price: midpoint price of that bin Value Area (VAH/VAL) using 70% volume The script builds the value area around the POC outward until it captures 70% of total volume: Start with the POC bin. Expand one bin at a time to the side with more volume. Stop when accumulated volume >= 70% of total profile volume. Then: VAL = rangeLow + binSize * lowerIdx VAH = rangeLow + binSize * (upperIdx + 1) This produces a classic “where most business happened” zone. VWMP (Volume-Weighted Mean Price) This is essentially the center of mass of the profile: VWMP = sum(price * volume ) / totalVolume It is similar in spirit to VWAP, but it is computed over the profile bins, not from bar-by-bar typical price. Skewness calculation: turning the profile into an oscillator This is the main feature. 1) Treat volumes as weights For each bin: weight = volume / totalVolume Now weights sum to 1. 2) Compute weighted mean Mean price: mean = sum(weight * price ) 3) Compute weighted variance and std deviation Variance: variance = sum(weight * (price - mean)^2) stdDev = sqrt(variance) 4) Compute weighted third central moment Third moment: m3 = sum(weight * (price - mean)^3) 5) Standardize to skewness Skewness: rawSkew = m3 / (stdDev^3) This standardization matters. Without it, the value would explode or shrink based on profile scale. Standardized skewness is dimensionless and comparable. Smoothing and regime rules Raw skewness can be jumpy because: profile bins change as rangeHigh/rangeLow shift, one high-volume candle can reshape the distribution, volume regimes change quickly in crypto. So the indicator applies EMA smoothing: smoothedSkew = EMA(rawSkew, smooth) Then it classifies regime using fixed thresholds: Bullish (ACCUMULATION): smoothedSkew > +0.25 Bearish (DISTRIBUTION): smoothedSkew < -0.25 Neutral: between those values Signals are generated on threshold cross events: Bull signal when smoothedSkew crosses above +0.25 Bear signal when smoothedSkew crosses below -0.25 This makes the skew act like a regime oscillator rather than a constantly flipping color. Volume Profile plotting modes The script draws the profile on the last bar, using boxes for each bin, anchored to the right with a configurable offset. The width of each profile bar is normalized by max bin volume: volRatio = binVol / maxVol barWidth = volRatio * width Three style modes exist: 1) Gradient Uses a “jet-like” gradient based on volRatio (blue → red). Higher-volume bins stand out naturally. Transparency increases as volume decreases, so low-volume bins fade. 2) Solid Uses the current regime color (bull/bear/neutral) for all bins, with transparency. This makes the profile read as “structure + regime.” 3) Skew Highlight Highlights bins that match the skew bias: If skew bullish, emphasize lower portion of profile. If skew bearish, emphasize higher portion of profile. Else, keep most bins neutral. This is a visual “where the skew is coming from” mode. Historical POC tracking and Naked POCs This script also treats POCs as meaningful levels over time, similar to how traders track old VA levels. What is a “naked POC”? A “naked POC” is a previously formed POC that has not been revisited (retested) by price since it was recorded. Many traders watch these as potential reaction zones because they represent prior “maximum traded interest” that the market has not re-engaged with. How this script records POCs It stores a new historical POC when: At least updatebars have passed since the last stored POC, and The POC has changed by at least pochangethres (%) from the last stored value. New stored POCs are flagged as naked by default. How naked becomes tested On each update, the script checks whether price has entered a small zone around a naked POC: zoneSize = POC * 0.002 (about 0.2%) If bar range overlaps that zone, mark it as tested (not naked). Display controls: Highlight Naked POCs: draws and labels untested POCs. Show Tested POCs: optionally draw tested ones in a muted color. To avoid clutter, the script limits stored POCs to the most recent 20 and avoids drawing ones too close to the current POC. On-chart key levels and what they mean When enabled, the script draws the current lookback profile levels on the price chart: POC (solid): the “most traded” price. VAH/VAL (dashed): boundaries of the 70% value area. VWMP (dotted): volume-weighted mean of the profile distribution. Interpretation framework (practical, not mystical): POC often behaves like a magnet in balanced conditions. VAH/VAL define the “accepted” area, breaks can signal auction continuation. VWMP is a fair-value reference, useful as a mean anchor when skew is neutralizing. Oscillator panel and histogram The skew oscillator is plotted in a separate pane: Line: smoothedSkew, colored by regime. Histogram: smoothedSkew as bars, colored by sign. Fill: subtle shading above/below 0 to reinforce bias. This makes it easy to read: Direction of bias (positive vs negative). Strength (distance from 0 and from thresholds). Transitions (crosses of ±0.25). Info table: what it summarizes On the last bar, a table prints key diagnostics: Current skew value (smoothed). Regime label (ACCUMULATION / DISTRIBUTION / NEUTRAL). Current POC, VAH, VAL, VWMP. Count of naked POCs still active. A simple “volume location” hint (lower/higher/balanced). This is designed for quick scanning without reading the entire profile. Alerts The indicator includes alerts for: Skew regime shifts (cross above +0.25, cross below -0.25). Price crossing above/below current POC. Approaching a naked POC (within 1% of any active naked POC). The “approaching naked POC” alert is useful as a heads-up that price is entering a historically important volume magnet/reaction zone. How to use it properly 1) Regime filter Use skew regime to decide what type of trades you should prioritize: ACCUMULATION (positive skew): market activity is heavier at lower prices, pullbacks into value or below VWMP often matter more. DISTRIBUTION (negative skew): activity is heavier at higher prices, rallies into value or above VWMP often matter more. NEUTRAL: mean-reversion and POC magnet behavior tends to dominate. This is not “buy when green.” It is context for what the auction is doing. 2) Level-based execution Combine skew with VA/POC levels: In neutral regimes, expect rotations around POC and inside VA. In strong skew regimes, watch for acceptance away from POC and reactions at VA edges. 3) Naked POCs as targets and reaction zones Naked POCs can act like unfinished business. Common workflows: As targets in rotations. As areas to reduce risk when price is approaching. As “if it breaks cleanly, trend continuation” markers when price returns with force. Parameter tuning guidance Lookback Controls how “local” the profile is. Shorter: reacts faster, more sensitive to recent moves. Longer: more stable, better for swing context. Bins Controls resolution of the profile. Higher bins: more detail, more computation, more sensitive profile shape. Lower bins: smoother, less detail, more stable skew. Smoothing Controls how noisy the skew oscillator is. Higher smoothing: fewer regime flips, slower response. Lower smoothing: more responsive, more false transitions. POC tracking settings Update interval and threshold decide how many historical POCs you store and how different they must be. If you set them too loose, you will spam levels. If too strict, you will miss meaningful shifts. Limitations and what not to assume This indicator uses candle-range volume distribution because Pine cannot see tick-level volume-at-price. That means: The profile is an approximation of where volume could have traded, not exact tape data. Skew is best treated as a structural bias, not a precise signal generator. Extreme single-bar events can distort the distribution briefly, smoothing helps but cannot remove reality. Summary Volume Profile Skew takes standard volume profile structure (POC, Value Area, volume-weighted mean) and adds a statistically grounded measure of profile shape using skewness. The result is a regime oscillator that quantifies whether volume concentration is leaning toward lower prices (accumulation) or higher prices (distribution), while also plotting the full profile, key levels, and historical naked POCs for actionable context.
Chỉ báo Pine Script®
của BackQuant
2
Hedge Fund Session Ranges [GMT+2] - Multi-Timezone TrackingOverview This professional-grade tool is designed for institutional-style trading, specifically focusing on the Liquidity Cycles of the global markets. It allows traders to visualize key trading windows (Asia, Europe, and US) with precision, using a fixed GMT+2 offset—ideal for traders aligned with Middle Eastern or Eastern European timezones. Key Features Triple Session Tracking: Includes pre-defined windows for Asia, London Morning, and NY Afternoon. Dynamic Box Scaling: Automatically calculates and visualizes the High/Low range of each session in real-time. GMT+2 Optimization: Built-in timezone handling to ensure your charts align perfectly with local bank hours. Clean Visuals: Minimalist design to avoid chart clutter, allowing for clear price action analysis. Why Trade Sessions? Institutional volume isn't distributed evenly throughout the day. By identifying the Asian Range (01:00-06:00), the London Open (10:00-12:00), and the NY Reversal/Trend (16:30-18:30), traders can identify "Liquidity Grabs" and "Expansion Phases" more effectively.
Chỉ báo Pine Script®
của netha0141
LDEF SENS Loss Dependent Error Filter Dominance Regime SwitchCAPITALCOM:GOLD LDEF SENS stands for Loss Dependent Error Filter. This indicator is a dominance regime filter with an adaptive switch boundary. It separates the market into two main states. Directional tradeable tape (trend and impulse conditions) Balanced noisy tape (higher fakeout probability) It also provides a dominance direction bias (bull vs bear) and an adaptive boundary you can use as a market switch signal. What you see in the indicator pane (bottom panel) Main line (0 to 100): dominance sensitivity score Line color meaning Green: bullish dominance (L greater than R) Red: bearish dominance (R greater than L) Gray: low strength or mixed tape Purple line: adaptive regime boundary (moving threshold) Violet shading: regime ON (tradeable conditions) Key idea: height equals strength, color equals direction, violet shading equals regime state. How to read the three images Image A - Regime ON in a trending environment Where to look Price panel: left to middle shows a clean up move Indicator panel: directly below the same time window Violet band is present for a sustained stretch Main line stays high and mostly green What it means When the violet band stays ON, the tape is directional enough for trend following setups to have higher quality. This is not an entry signal. It is an environment filter. Image B - Switch boundary and state changes Where to look Indicator panel: focus on the purple adaptive line and the main line crossing relative to it Watch the moment the main line moves above the purple line. In the same region, violet shading turns ON. What it means The purple line is the adaptive regime boundary. Cross above: regime switches toward directional tape (state change confirmation) Cross below: regime fades and chop risk returns Image C - Direction semantics inside a regime Where to look Indicator panel: inside violet shaded regions Main line is green during bullish dominance (L greater than R) Main line is red during bearish dominance (R greater than L) What it means Violet answers: is this a tradeable regime Green or red answers: which side is dominating Together, they provide a filter plus bias framework. Practical usage Regime filter Prefer setups only when the violet band is ON Reduce size or tighten criteria when the violet band is OFF Direction bias Prefer longs when the line is green Prefer shorts when the line is red Treat gray as no edge or mixed tape Switch boundary analysis Cross above purple: treat as regime shift confirmation Cross below purple: treat as regime cooling off and higher chop risk Limitations This is a regime and dominance tool, not a standalone entry generator. Regime confirmation can be late by design, especially after shocks. Use it with structure, liquidity, and risk management.
Chỉ báo Pine Script®
của TheWannaBeQuant
5
Market Structure & Supply-Demand EngineMarket Structure & Supply-Demand Engine (MSD-Engine) is a professional, non-repainting market structure and supply-demand analysis tool built purely on price action and volatility logic. This indicator is designed for discretionary traders who want a clean, institutional-style view of market structure without lagging indicators or strategy automation. 🔍 What This Indicator Does MSD-Engine identifies major structural reversals, plots price-action based supply & demand zones, and provides multi-timeframe confluence in a single, unified framework. It is visual and analytical only — no strategy orders, no backtesting, and no repainting. 🚀 Core Features • Non-Repainting Market Structure Event-based swing reversal detection ATR-adaptive displacement filtering Confirmed pivots only (no future leaks) • Pure Supply & Demand Zones Candle-structure based zone detection Volume-weighted zone strength Automatic invalidation on breach Configurable zone limits to maintain chart clarity • Multi-Timeframe Context (MTF) Chart timeframe structure Two independent higher-timeframe supply & demand layers Higher-timeframe directional bias visualization HTF zones plotted only on confirmed HTF closes • Volatility-Adaptive Logic ATR normalized across timeframes Dynamic reversal thresholds Stable behavior from scalping to swing charts • Trendline Lifecycle Tracking Automatic major trendline construction Single-fire break detection Break validation / failure logic HTF-aligned vs counter-trend classification 🧠 Designed For • Discretionary price-action traders • Supply & demand traders • Market structure & smart-money style analysis • Multi-timeframe confluence trading • Futures, indices, forex, crypto, and equities ⚠️ Important Notes This is NOT a strategy or auto-trading system No buy/sell signals or performance metrics No repainting (uses barmerge.lookahead_off) Educational & analytical use only 📜 Disclaimer This script is provided for educational and analytical purposes only. It does not constitute financial advice. Trading financial markets involves risk.
Chỉ báo Pine Script®
của Market_Logic_India
2
Jurik MA Trend Breakouts [BigBeluga]🔵 OVERVIEW Jurik MA Trend Breakouts is a precision trend-breakout detector built on a custom Jurik-smoothed moving average. It identifies trend direction with ultra-low lag and maps breakout levels using pivot-based swing highs/lows. The indicator plots dynamic breakout lines and confirms trend continuation or reversal when price breaks them — providing clean, minimalistic yet extremely accurate trend signals. 🔵 CONCEPTS Jurik Moving Average (JMA) — A highly smooth and low-lag moving average that reacts quickly to trend shifts without noise. This becomes the core trend baseline. Trend Bias — • JMA rising → bullish trend • JMA falling → bearish trend The JMA color updates instantly based on slope. Swing Pivots — Recent pivot highs/lows are detected to define structural break levels while filtering out weak noise. Trend Breakout Levels — The indicator draws horizontal levels at the last valid pivot in the direction of the trend. These levels act as “confirmation gates” for breakout entries. ATR Validity Filter — Ensures only meaningful pivots within a threshold are used to prevent fake breakouts. 🔵 FEATURES Ultra-Smooth Jurik Trend Line — A visually clean trend baseline changing color based on direction. Automatic Swing High Breakout Setup (Bullish) — • During an uptrend, the indicator tracks the most recent pivot high. • A horizontal breakout line is extended across the chart. • A ✔ marker appears at both pivot points when the breakout structure becomes valid. Automatic Swing Low Breakout Setup (Bearish) — • During a downtrend, pivot lows are tracked. • A horizontal breakout line marks the breakdown level. • ✔ markers confirm valid structure before the breakout triggers. Breakout Detection — • Price closing above the bullish breakout line → “↑” signal printed on the chart. • Price closing below the bearish breakout line → “↓” signal printed on the chart. Automatic Reset on Trend Change — When the JMA trend flips, all breakout structures are cleared and the model starts tracking new pivot levels. Trend-Colored Visualization — Glow + main JMA line give instant clarity of market direction. 🔵 HOW IT WORKS 1. JurikMA defines the main trend — Slope determines bullish or bearish state. 2. The indicator continuously searches for pivots in the direction of the trend. 3. When a valid pivot forms and passes ATR proximity filter, a structural breakout level is drawn. 4. As long as price stays below that level (bullish case), the trend setup remains active. 5. When price finally breaks the level , the indicator prints a directional arrow (↑ or ↓). 6. Trend flip instantly resets all levels and begins tracking pivots on the opposite side. 🔵 HOW TO USE Breakout Trading — Enter long on “↑” and short on “↓” signals when price breaks key pivot structure. Trend Confirmation — Use the JurikMA color to stay aligned with the main trend direction. Reversals — Trend flips often mark major turning points. Structure Mapping — Use the horizontal breakout lines to understand how close price is to confirming a new trend leg. 🔵 CONCLUSION Jurik MA Trend Breakouts combines the speed of a Jurik MA with structural breakout logic to deliver clean, reliable entry signals. Its minimal design, pivot-based confirmation, and trend-aligned logic make it suitable for scalping, swing trading, and intraday trend continuation setups. If you want fast yet filtered breakout recognition with almost zero noise, this tool gives you everything you need.
Chỉ báo Pine Script®
của BigBeluga
21