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Eclipse Multi-Oscillator [JOAT]Eclipse Multi-Oscillator - Unified Momentum Confluence System Introduction and Purpose Eclipse Multi-Oscillator is an open-source indicator that combines four classic oscillators (RSI, Stochastic, CCI, and Williams %R) into a single unified view with confluence detection. The core problem this indicator solves is oscillator disagreement: traders often see RSI oversold while Stochastic is neutral, or CCI overbought while Williams %R is mid-range. This creates confusion about the true momentum state. This indicator addresses that by displaying all four oscillators together and counting how many agree on overbought or oversold conditions, providing a clear confluence score that cuts through the noise. Why These Four Oscillators Work Together Each oscillator measures momentum differently, and their combination provides a more complete picture: 1. RSI (Relative Strength Index) - Measures the magnitude of recent price changes. Best at identifying momentum exhaustion. 2. Stochastic - Compares closing price to the high-low range. Best at identifying where price is within its recent range. 3. CCI (Commodity Channel Index) - Measures price deviation from statistical mean. Best at identifying unusual price movements. 4. Williams %R - Similar to Stochastic but inverted. Provides confirmation of Stochastic readings. When 3 or more of these oscillators agree on overbought or oversold, the signal is significantly more reliable than any single oscillator alone. How Confluence Scoring Works The indicator counts how many oscillators are in extreme territory: int obCount = 0 if rsi > rsiOB obCount += 1 if stochK > stochOB obCount += 1 if cci > cciOB obCount += 1 if willRScaled > stochOB obCount += 1 bool strongOverbought = obCount >= 3 bool strongOversold = osCount >= 3 The confluence score ranges from -4 (all oversold) to +4 (all overbought), with 0 being neutral. Signal Types Strong Oversold - 3+ oscillators below oversold threshold (potential bounce) Strong Overbought - 3+ oscillators above overbought threshold (potential pullback) OB/OS Exit - RSI leaving extreme zone with Stochastic confirmation (potential reversal) Divergence - Price makes new high/low while RSI does not (potential reversal warning) Dashboard Information RSI/Stoch K/CCI/Will %R - Current values with zone status (OB/OS/MID) Confluence - Overall bias (STRONG OS, STRONG OB, Lean Bull/Bear, Neutral) OB Count - How many oscillators are overbought (0-4) OS Count - How many oscillators are oversold (0-4) How to Use This Indicator For Reversal Trading: 1. Wait for Strong Oversold (3+ oscillators agree) 2. Look for bullish candlestick pattern or support level 3. Enter long with stop below recent low 4. Take profit when confluence returns to neutral or overbought For Trend Confirmation: 1. Check confluence direction matches your trade bias 2. Avoid longs when confluence is strongly overbought 3. Avoid shorts when confluence is strongly oversold For Divergence Trading: 1. Watch for "D" labels indicating RSI divergence 2. Bullish divergence at support = potential long 3. Bearish divergence at resistance = potential short Input Parameters RSI Length (14) - Period for RSI calculation Stochastic K/D Length (14/3) - Periods for Stochastic CCI Length (20) - Period for CCI Williams %R Length (14) - Period for Williams %R OB/OS Thresholds - Customizable levels for each oscillator Timeframe Recommendations 15m-1H: Good for intraday momentum analysis 4H-Daily: Best for swing trading confluence Very short timeframes may produce noisy signals Limitations All oscillators can remain in extreme territory during strong trends Confluence does not predict direction, only identifies extremes Divergence detection is simplified and may miss some patterns Works best in ranging or moderately trending markets Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied. This indicator does not constitute financial advice. Oscillator confluence does not guarantee reversals. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
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Aurora Volatility Bands [JOAT]Aurora Volatility Bands - Dynamic ATR-Based Envelope System Introduction and Purpose Aurora Volatility Bands is an open-source overlay indicator that creates multi-layered volatility envelopes around price using ATR (Average True Range) calculations. The core problem this indicator solves is that static bands (like fixed percentage envelopes) fail to adapt to changing market conditions. During high volatility, static bands are too tight; during low volatility, they're too wide. This indicator addresses that by using ATR-based dynamic bands that automatically expand during volatile periods and contract during quiet periods, providing contextually appropriate support/resistance levels at all times. Why These Components Work Together The indicator combines three analytical approaches: 1. Triple-Layer Band System - Inner (1x ATR), Outer (2x ATR), and Extreme (3x ATR) bands provide graduated levels of significance 2. Volatility State Detection - Compares current ATR to historical average to classify market regime 3. Multiple MA Types - Allows customization of the center line calculation method These components complement each other: The triple-layer system gives traders multiple reference points - inner bands for normal moves, outer for significant moves, extreme for rare events Volatility state detection tells you WHEN bands are expanding or contracting, helping anticipate breakouts or mean-reversion MA type selection lets you match the indicator to your trading style (faster EMA vs smoother SMA) How the Calculation Works The bands are calculated using ATR multiplied by configurable factors: float atr = ta.atr(atrPeriod) float innerUpper = centerMA + (atr * innerMult) float outerUpper = centerMA + (atr * outerMult) float extremeUpper = centerMA + (atr * extremeMult) Volatility state is determined by comparing current ATR percentage to its historical average: float atrPercent = (atr / close) * 100 float avgAtrPercent = ta.sma(atrPercent, volatilityLookback) float volatilityRatio = atrPercent / avgAtrPercent bool isExpanding = volatilityRatio > 1.2 // 20%+ above average bool isContracting = volatilityRatio < 0.8 // 20%+ below average Signal Types Band Touch - Price reaches inner, outer, or extreme bands Mean Reversion - Price returns to center after touching outer/extreme bands Breakout - Sustained move beyond outer bands during volatility expansion Dashboard Information Volatility - Current state (EXPANDING/CONTRACTING/NORMAL) Vol Ratio - Current volatility vs average (e.g., 1.5x = 50% above average) ATR - Current ATR value ATR % - ATR as percentage of price Zone - Current price position (EXTREME HIGH/UPPER ZONE/CENTER ZONE/etc.) Position - Price position as percentage within band structure Width - Total band width as percentage of price Using SMA in settings: How to Use This Indicator For Mean-Reversion Trading: 1. Wait for price to touch outer or extreme bands 2. Check that volatility state is NORMAL or CONTRACTING (not expanding) 3. Look for reversal candlestick patterns at the band 4. Enter toward center MA with stop beyond the band For Breakout Trading: 1. Wait for volatility state to show EXPANDING 2. Look for price closing beyond outer bands 3. Enter in direction of breakout 4. Use the band as trailing stop reference For Volatility Analysis: 1. Monitor volatility ratio for regime changes 2. CONTRACTING often precedes large moves (squeeze) 3. EXPANDING confirms trend strength Using VWMA and Mean Reversion Signal/MR: Input Parameters ATR Period (14) - Period for ATR calculation Inner/Outer/Extreme Multipliers (1.0/2.0/3.0) - Band distance from center MA Type (EMA) - Center line calculation method MA Period (20) - Period for center line Volatility Comparison Period (20) - Lookback for volatility state Timeframe Recommendations 15m-1H: Good for intraday mean-reversion 4H-Daily: Best for swing trading and breakout identification Weekly: Useful for position trading and major level identification Limitations ATR-based bands lag during sudden volatility spikes Mean-reversion signals can fail in strong trends Breakout signals may whipsaw in ranging markets Works best on liquid instruments with consistent volatility patterns Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works. This indicator does not constitute financial advice. Band touches do not guarantee reversals. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses. - Made with passion by officialjackofalltrades
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Quantum Reversal Detector [JOAT] Quantum Reversal Detector - Multi-Factor Reversal Probability Analysis Introduction and Purpose Quantum Reversal Detector is an open-source overlay indicator that combines multiple reversal detection methods into a unified probability-based framework. The core problem this indicator addresses is the unreliability of single-factor reversal signals. A price touching support means nothing without momentum confirmation; an RSI oversold reading means nothing without price structure context. This indicator solves that by requiring multiple independent factors to align before generating reversal signals, then expressing the result as a probability score rather than a binary signal. Why These Components Work Together The indicator combines five analytical approaches, each addressing a different aspect of reversal detection: 1. RSI Extremes - Identifies momentum exhaustion (overbought/oversold) 2. MACD Crossovers - Confirms momentum direction change 3. Support/Resistance Proximity - Ensures price is at a significant level 4. Multi-Depth Momentum - Analyzes momentum across multiple timeframes 5. Statistical Probability - Quantifies reversal likelihood using Bayesian updating These components are not randomly combined. Each filter catches reversals that others miss: RSI catches momentum exhaustion but misses structural reversals MACD catches momentum shifts but lags price action S/R proximity catches structural levels but ignores momentum Multi-depth momentum catches divergences across timeframes Probability scoring combines all factors into actionable confidence levels How the Detection System Works Step 1: Pattern Detection The indicator first identifies potential reversal conditions: // Check if price is at support/resistance float lowestLow = ta.lowest(low, period) float highestHigh = ta.highest(high, period) bool atSupport = low <= lowestLow * 1.002 bool atResistance = high >= highestHigh * 0.998 // Check RSI conditions float rsi = ta.rsi(close, 14) bool oversold = rsi < 30 bool overbought = rsi > 70 // Check MACD crossover float macd = ta.ema(close, 12) - ta.ema(close, 26) float signal = ta.ema(macd, 9) bool macdBullish = ta.crossover(macd, signal) bool macdBearish = ta.crossunder(macd, signal) // Combine for reversal detection if atSupport and oversold and macdBullish bullishReversal := true Step 2: Multi-Depth Momentum Analysis The indicator calculates momentum across multiple periods to detect divergences: calculateQuantumMomentum(series float price, simple int period, simple int depth) => float totalMomentum = 0.0 for i = 0 to depth - 1 int currentPeriod = period * (i + 1) float momentum = ta.roc(price, currentPeriod) totalMomentum += momentum totalMomentum / depth This creates a composite momentum reading that smooths out noise while preserving genuine momentum shifts. Step 3: Bayesian Probability Calculation The indicator uses Bayesian updating to calculate reversal probability: bayesianProbability(series float priorProb, series float likelihood, series float evidence) => float posterior = evidence > 0 ? (likelihood * priorProb) / evidence : priorProb math.min(math.max(posterior, 0.0), 1.0) The prior probability starts at 50% and updates based on: RSI extreme readings increase likelihood MACD crossovers increase likelihood S/R proximity increases likelihood Momentum divergence increases likelihood Step 4: Confidence Intervals Using Monte Carlo simulation concepts, the indicator estimates price distribution: monteCarloSimulation(series float price, series float volatility, simple int iterations) => float sumPrice = 0.0 float sumSqDiff = 0.0 for i = 0 to iterations - 1 float randomFactor = (i % 10 - 5) / 10.0 float simulatedPrice = price + volatility * randomFactor sumPrice += simulatedPrice float avgPrice = sumPrice / iterations // Calculate standard deviation for confidence intervals This provides 95% and 99% confidence bands around the current price. Signal Classification Signals are classified by confirmation level: Confirmed Reversal : Pattern detected for N consecutive bars (default 3) High Probability : Confirmed + Bayesian probability > 70% Ultra High Probability : High probability + PDF above average Dashboard Information The dashboard displays: Bayesian Probability - Updated reversal probability (0-100%) Quantum Momentum - Multi-depth momentum average RSI - Current RSI value with overbought/oversold status Volatility - Current ATR as percentage of price Reversal Signal - BULLISH, BEARISH, or NONE Divergence - Momentum divergence detection MACD - Current MACD histogram value S/R Zone - AT SUPPORT, AT RESISTANCE, or NEUTRAL 95% Confidence - Price range with 95% probability Bull/Bear Targets - ATR-based reversal targets Visual Elements Quantum Bands - ATR-based upper and lower channels Probability Field - Circle layers showing probability distribution Confidence Bands - 95% and 99% confidence interval circles Reversal Labels - REV markers at confirmed reversals High Probability Markers - Star diamonds at high probability setups Reversal Zones - Boxes around confirmed reversal areas Divergence Markers - Triangles at momentum divergences How to Use This Indicator For Reversal Trading: 1. Wait for Bayesian Probability to exceed 70% 2. Confirm price is at S/R zone (dashboard shows AT SUPPORT or AT RESISTANCE) 3. Check that RSI is in extreme territory (oversold for longs, overbought for shorts) 4. Enter when REV label appears with high probability marker For Risk Management: 1. Use the 95% confidence band as a stop-loss reference 2. Use Bull/Bear Targets for take-profit levels 3. Higher probability readings warrant larger position sizes For Filtering False Signals: 1. Increase Confirmation Bars to require more consecutive signals 2. Only trade when probability exceeds 70% 3. Require divergence confirmation for highest conviction Input Parameters Reversal Period (21) - Lookback for S/R and momentum calculations Quantum Depth (5) - Number of momentum layers for multi-depth analysis Confirmation Bars (3) - Consecutive bars required for confirmation Detection Sensitivity (1.2) - Band width and target multiplier Bayesian Probability (true) - Enable probability calculation Monte Carlo Simulation (true) - Enable confidence interval calculation Normal Distribution (true) - Enable PDF calculation Confidence Intervals (true) - Enable confidence bands Timeframe Recommendations 1H-4H: Best for swing trading reversals Daily: Fewer but more significant reversal signals 15m-30m: More signals, requires higher probability threshold Limitations Statistical concepts are simplified implementations for Pine Script Monte Carlo uses deterministic pseudo-random factors, not true randomness Bayesian probability uses simplified prior/likelihood model Reversal detection does not guarantee actual reversals will occur Confirmation bars add lag to signal generation Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works. This indicator does not constitute financial advice. Reversal detection is probabilistic, not predictive. The probability scores represent statistical likelihood based on historical patterns, not guaranteed outcomes. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses. - Made with passion by officialjackofalltrades
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Photon Price Action Scanner [JOAT]Photon Price Action Scanner - Multi-Pattern Recognition with Adaptive Filtering Introduction and Purpose Photon Price Action Scanner is an open-source overlay indicator that automates the detection of 15+ candlestick patterns while filtering them through multiple confirmation layers. The core problem this indicator solves is pattern noise: raw candlestick pattern detection produces too many signals, most of which fail because they lack context. This indicator addresses that by combining pattern recognition with trend alignment, volume-weighted strength scoring, velocity confirmation, and an adaptive neural bias filter. The combination of these components is not arbitrary. Each filter addresses a specific weakness in standalone pattern detection: Trend alignment ensures patterns appear in favorable market structure Volume-weighted strength filters out weak patterns with low conviction Velocity confirmation identifies momentum behind the pattern Neural bias filter adapts to recent price behavior to avoid counter-trend signals What Makes This Indicator Original While candlestick pattern scanners exist, this indicator's originality comes from: 1. Multi-Layer Filtering System - Patterns must pass through trend, strength, velocity, and neural bias filters before generating signals. This dramatically reduces false positives compared to simple pattern detection. 2. Adaptive Neural Bias Filter - A custom momentum-adjusted EMA that learns from recent price action using a configurable learning rate. This is not a standard moving average but an adaptive filter that accelerates during trends and smooths during consolidation. 3. Pattern Strength Scoring - Each pattern receives a strength score based on volume ratio and body size, allowing traders to focus on high-conviction setups rather than every pattern occurrence. 4. Smart Cooldown System - Prevents signal overlap by enforcing minimum bar spacing between pattern labels, keeping charts clean even when "Show All Patterns" is enabled. How the Components Work Together Step 1: Pattern Detection The indicator scans for 15 candlestick patterns using precise mathematical definitions: // Example: Bullish Engulfing requires the current bullish candle to completely // engulf the previous bearish candle with a larger body isBullishEngulfing() => bool pattern = close < open and close > open and open <= close and close >= open and close - open > open - close pattern // Example: Three White Soldiers requires three consecutive bullish candles // with each opening within the previous body and closing higher isThreeWhiteSoldiers() => bool pattern = close > open and close > open and close > open and close < close and close < close and open > open and open < close and open > open and open < close pattern Step 2: Strength Calculation Each detected pattern receives a strength score combining volume and body size: float volRatio = avgVolume > 0 ? volume / avgVolume : 1.0 float bodySize = math.abs(close - open) / close float baseStrength = (volRatio + bodySize * 100) / 2 This ensures patterns with above-average volume and large bodies score higher than weak patterns on low volume. Step 3: Trend Alignment Patterns are checked against the trend direction using an EMA: float trendEMA = ta.ema(close, i_trendPeriod) int trendDir = close > trendEMA ? 1 : close < trendEMA ? -1 : 0 Bullish patterns in uptrends and bearish patterns in downtrends receive priority. Step 4: Neural Bias Filter The adaptive filter uses a momentum-adjusted EMA that responds to price changes: neuralEMA(series float src, simple int period, simple float lr) => var float neuralValue = na var float momentum = 0.0 if na(neuralValue) neuralValue := src float error = src - neuralValue float adjustment = error * lr momentum := momentum * 0.9 + adjustment * 0.1 neuralValue := neuralValue + adjustment + momentum neuralValue The learning rate (lr) controls how quickly the filter adapts. Higher values make it more responsive; lower values make it smoother. Step 5: Velocity Confirmation Price velocity (rate of change) must exceed the average velocity for strong signals: float velocity = ta.roc(close, i_trendPeriod) float avgVelocity = ta.sma(velocity, i_trendPeriod) bool velocityBull = velocity > avgVelocity * 1.5 Step 6: Signal Classification Signals are classified based on how many filters they pass: Strong Pattern : Pattern + strength threshold + trend alignment + neural bias + velocity Ultra Pattern : Strong pattern + gap in same direction + velocity confirmation Watch Pattern : Pattern detected but not all filters passed Detected Patterns Classic Reversal Patterns: Bullish/Bearish Engulfing - Complete body engulfment with larger body Hammer - Long lower wick (2x body), small upper wick, bullish context Shooting Star - Long upper wick (2x body), small lower wick, bearish context Morning Star - Three-bar bullish reversal with small middle body Evening Star - Three-bar bearish reversal with small middle body Piercing Line - Bullish candle closing above midpoint of previous bearish candle Dark Cloud Cover - Bearish candle closing below midpoint of previous bullish candle Bullish/Bearish Harami - Small body contained within previous larger body Doji - Body less than 10% of total range (indecision) Advanced Patterns (Optional): Three White Soldiers - Three consecutive bullish candles with rising closes Three Black Crows - Three consecutive bearish candles with falling closes Tweezer Top - Equal highs with reversal candle structure Tweezer Bottom - Equal lows with reversal candle structure Island Reversal - Gap isolation creating reversal structure Dashboard Information The dashboard displays real-time analysis: Pattern - Current detected pattern name or "SCANNING..." Bull/Bear Strength - Volume-weighted strength scores Trend - UPTREND, DOWNTREND, or SIDEWAYS based on EMA RSI - 14-period RSI for momentum context Momentum - 10-period momentum reading Volatility - ATR as percentage of price Neural Bias - BULLISH, BEARISH, or NEUTRAL from adaptive filter Action - ULTRA BUY/SELL, BUY/SELL, WATCH BUY/SELL, or WAIT Visual Elements Pattern Labels - Abbreviated codes (BE=Engulfing, H=Hammer, MS=Morning Star, etc.) Neural Bias Line - Adaptive trend line showing filter direction Gap Boxes - Cyan boxes highlighting price gaps Action Zones - Dashed boxes around strong pattern areas Velocity Markers - Small circles when velocity confirms direction Ultra Signals - Large labels for highest conviction setups How to Use This Indicator For Reversal Trading: 1. Wait for a pattern to appear at a key support/resistance level 2. Check that the Action shows "BUY" or "SELL" (not just "WATCH") 3. Confirm the Neural Bias aligns with your trade direction 4. Use the strength score to gauge conviction (higher is better) For Trend Continuation: 1. Identify the trend using the Trend row in the dashboard 2. Look for patterns that align with the trend (bullish patterns in uptrends) 3. Ultra signals indicate the strongest continuation setups For Filtering Noise: 1. Keep "Show All Patterns" disabled to see only filtered signals 2. Increase "Pattern Strength Filter" to see fewer, higher-quality patterns 3. Enable "Velocity Confirmation" to require momentum behind patterns Input Parameters Scan Sensitivity (1.0) - Overall detection sensitivity multiplier Pattern Strength Filter (3) - Minimum strength score for strong signals Trend Period (20) - EMA period for trend determination Show All Patterns (false) - Display all patterns regardless of filters Advanced Patterns (true) - Enable soldiers/crows/tweezer detection Gap Analysis (true) - Enable gap detection and boxes Velocity Confirmation (true) - Require velocity for strong signals Neural Bias Filter (true) - Enable adaptive trend filter Neural Period (50) - Lookback for neural bias calculation Neural Learning Rate (0.12) - Adaptation speed (0.01-0.5) Timeframe Recommendations 1H-4H: Best balance of signal frequency and reliability Daily: Fewer but more significant patterns 15m-30m: More signals, requires tighter filtering (increase strength threshold) Limitations Pattern detection is mechanical and does not consider fundamental context Neural bias filter may lag during rapid trend reversals Gap detection requires clean price data without after-hours gaps Strength scoring favors high-volume patterns, which may miss valid low-volume setups - Made with passion by officialjackofalltrades
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Volume-Weighted Price Z-Score [QuantAlgo]🟢 Overview The Volume-Weighted Price Z-Score indicator quantifies price deviations from volume-weighted equilibrium using statistical standardization. It combines volume-weighted moving average analysis with logarithmic deviation measurement and volatility normalization to identify when prices have moved to statistically extreme levels relative to their volume-weighted baseline, helping traders and investors spot potential mean reversion opportunities across multiple timeframes and asset classes. 🟢 How It Works The indicator's core methodology lies in its volume-weighted statistical approach, where price displacement is measured through normalized deviations from volume-weighted price levels: volumeWeightedAverage = ta.vwma(priceSource, lookbackPeriod) logDeviation = math.log(priceSource / volumeWeightedAverage) volatilityMeasure = ta.stdev(logDeviation, lookbackPeriod) The script uses logarithmic transformation to capture proportional price changes rather than absolute differences, ensuring equal treatment of percentage moves regardless of price level: rawZScore = logDeviation / volatilityMeasure zScore = ta.ema(rawZScore, smoothingPeriod) First, it establishes the volume-weighted baseline which gives greater weight to price levels where significant trading occurred, creating a more representative equilibrium point than simple moving averages. Then, the logarithmic deviation measurement converts the price-to-average ratio into a normalized scale: logDeviation = math.log(priceSource / volumeWeightedAverage) Next, statistical normalization is achieved by dividing the deviation by its own historical volatility, creating a standardized z-score that measures how many standard deviations the current price sits from the volume-weighted mean. Finally, EMA smoothing filters noise while preserving the signal's responsiveness to genuine market extremes: rawZScore = logDeviation / volatilityMeasure zScore = ta.ema(rawZScore, smoothingPeriod) This creates a volume-anchored statistical oscillator that combines price-volume relationship analysis with volatility-adjusted normalization, providing traders with probabilistic insights into market extremes and mean reversion potential based on standard deviation thresholds. 🟢 Signal Interpretation ▶ Positive Values (Above Zero): Price trading above volume-weighted average indicating potential overvaluation relative to volume-weighted equilibrium = Caution on longs, potential mean reversion downward = Short/sell opportunities ▶ Negative Values (Below Zero): Price trading below volume-weighted average indicating potential undervaluation relative to volume-weighted equilibrium = Caution on shorts, potential mean reversion upward = Long/buy opportunities ▶ Zero Line Crosses: Mean reversion transitions where price crosses back through volume-weighted equilibrium, indicating shift from overvalued to undervalued (or vice versa) territory ▶ Extreme Positive Zone (Above +2.5σ default): Statistically rare overvaluation representing 98.8%+ confidence level deviation, indicating extremely stretched bullish conditions with high mean reversion probability = Strong correction warning/short signal ▶ Extreme Negative Zone (Below -2.5σ default): Statistically rare undervaluation representing 98.8%+ confidence level deviation, indicating extremely stretched bearish conditions with high mean reversion probability = Strong buying opportunity signal ▶ ±1σ Reference Levels: Moderate deviation zones (±1 standard deviation) marking common price fluctuation boundaries where approximately 68% of price action occurs under normal distribution ▶ ±2σ Reference Levels: Significant deviation zones (±2 standard deviations) marking unusual price extremes where approximately 95% of price action should be contained under normal conditions 🟢 Features ▶ Preconfigured Presets: Three optimized parameter sets accommodate different analytical approaches, instruments and timeframes. "Default" provides balanced statistical measurement suitable for swing trading and daily/4-hour analysis, offering deviation detection with moderate responsiveness to price dislocations. "Fast Response" delivers heightened sensitivity optimized for intraday trading and scalping on 15-minute to 1-hour charts, using shorter statistical windows and minimal smoothing to capture rapid mean reversion opportunities as they develop. "Smooth Trend" offers conservative extreme identification ideal for position trading on daily to weekly charts, employing extended statistical periods and heavy noise filtering to isolate only the most significant market extremes. ▶ Built-in Alerts: Seven alert conditions enable comprehensive automated monitoring of statistical extremes and mean reversion events. Extreme Overbought triggers when z-score crosses above the extreme threshold (default +2.5σ) signaling rare overvaluation, Extreme Oversold activates when z-score crosses below the negative extreme threshold (default -2.5σ) signaling rare undervaluation. Exit Extreme Overbought and Exit Extreme Oversold alert when prices begin reverting from these statistical extremes back toward the mean. Bullish Mean Reversion notifies when z-score crosses above zero indicating shift to overvalued territory, while Bearish Mean Reversion triggers on crosses below zero indicating shift to undervalued territory. Any Extreme Level provides a combined alert for any extreme threshold breach regardless of direction. These notifications allow you to capitalize on statistically significant price dislocations without continuous chart monitoring. ▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and visual preferences, ensuring optimal contrast for identifying positive versus negative deviations across trading environments. The adjustable fill transparency control (0-100%) allows fine-tuning of the gradient area prominence between the z-score line and zero baseline, with higher opacity values creating subtle background context while lower values produce bold deviation emphasis. Optional bar coloring extends the z-score gradient directly to the indicator pane bars, providing immediate visual reinforcement of current deviation magnitude and direction without requiring reference to the plotted line itself. *Note: This indicator requires volume data to function correctly, as it calculates deviations from a volume-weighted price average. Tickers with no volume data or extremely limited volume will not produce meaningful results, i.e., the indicator may display flat lines, erratic values, or fail to calculate properly. Using this indicator on assets without volume data (certain forex pairs, synthetic indices, or instruments with unreported/unavailable volume) will produce unreliable or no results at all. Additionally, ensure your chart has sufficient historical data to cover the selected lookback period, e.g., using a 100-bar lookback on a chart with only 50 bars of history will yield incomplete or inaccurate calculations. Always verify your chosen ticker has consistent, accurate volume information and adequate price history before applying this indicator.
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Cosmic Volume Analyzer [JOAT] Cosmic Volume Analyzer - Astrophysics Edition Overview Cosmic Volume Analyzer is an open-source oscillator indicator that applies astrophysics-inspired concepts to volume analysis. It classifies volume into buy/sell categories, calculates volume flow, detects accumulation/distribution phases, identifies climax volume events, and uses gravitational and stellar mass analogies to visualize volume dynamics. What This Indicator Does The indicator calculates and displays: Volume Classification - Categorizes each bar as CLIMAX_BUY, CLIMAX_SELL, HIGH_BUY, HIGH_SELL, NORMAL_BUY, or NORMAL_SELL Volume Flow - Percentage showing buy vs sell pressure over a lookback period Buy/Sell Volume - Separated volume based on candle direction Accumulation/Distribution - Phase detection using Money Flow Multiplier Volume Oscillator - Fast vs slow volume EMA comparison Gravitational Pull - Volume-weighted price attraction metric Stellar Mass Index - Volume ratio combined with price momentum Black Hole Detection - Identifies extremely low volume periods (liquidity voids) Supernova Events - Detects extreme volume with extreme price movement Orbital Cycles - Sine-wave based cyclical visualization How It Works Volume classification uses volume ratio and candle direction: classifyVolume(series float vol, series float close, series float open) => float avgVol = ta.sma(vol, 20) float volRatio = avgVol > 0 ? vol / avgVol : 1.0 if volRatio > 1.5 if close > open classification := "CLIMAX_BUY" else classification := "CLIMAX_SELL" else if volRatio > 1.2 // HIGH_BUY or HIGH_SELL else // NORMAL_BUY or NORMAL_SELL Volume flow separates buy and sell volume over a period: calculateVolumeFlow(series float vol, series float close, simple int period) => float currentBuyVol = close > open ? vol : 0.0 float currentSellVol = close < open ? vol : 0.0 // Accumulate in buffers float flow = (buyVolume - sellVolume) / totalVol * 100 Accumulation/Distribution uses the Money Flow Multiplier: float mfm = ((close - low) - (high - close)) / (high - low) float mfv = mfm * vol float adLine = ta.cum(mfv) if adLine > adEMA and ta.rising(adLine, 3) phase := "ACCUMULATION" else if adLine < adEMA and ta.falling(adLine, 3) phase := "DISTRIBUTION" Gravitational pull uses volume-weighted price distance: gravitationalPull(series float vol, series float price, simple int period) => float massCenter = ta.vwma(price, period) float distance = math.abs(price - massCenter) float mass = vol / ta.sma(vol, period) float gravity = distance > 0 ? mass / (distance * distance) : 0.0 Signal Generation Signals are generated based on volume conditions: Buy Climax: Volume exceeds 2 standard deviations above average on bullish candle Sell Climax: Volume exceeds 2 standard deviations above average on bearish candle Strong Buy Flow: Volume flow exceeds positive threshold (default 45%) Strong Sell Flow: Volume flow exceeds negative threshold (default -45%) Supernova: Volume 3x average AND price change 3x average Black Hole: Volume 2 standard deviations below average Dashboard Panel (Top-Right) Volume Class - Current volume classification Volume Flow - Buy/sell flow percentage Buy Volume - Accumulated buy volume Sell Volume - Accumulated sell volume A/D Phase - ACCUMULATION/DISTRIBUTION/NEUTRAL Volume Strength - Normalized volume strength Gravity Pull - Current gravitational metric Stellar Mass - Current stellar mass index Cosmic Field - Combined cosmic field strength Black Hole - Detection status and void strength Signal - Current actionable status Visual Elements Volume Ratio Columns - Colored bars showing normalized volume Volume Flow Line - Main oscillator showing flow direction Flow EMA - Smoothed flow for trend reference Volume Oscillator - Area plot showing fast/slow comparison Gravity Field - Area plot showing gravitational pull Orbital Cycle - Circle plots showing cyclical pattern Stellar Mass Line - Line showing mass index Climax Markers - Fire emoji for buy climax, snowflake for sell climax Supernova Markers - Diamond shapes for extreme events Black Hole Markers - X-cross for liquidity voids A/D Phase Background - Subtle background color based on phase Input Parameters Volume Period (default: 20) - Period for volume calculations Distribution Levels (default: 5) - Granularity of distribution analysis Flow Threshold (default: 1.5) - Multiplier for flow significance Accumulation Period (default: 14) - Period for A/D calculation Gravitational Analysis (default: true) - Enable gravity metrics Black Hole Detection (default: true) - Enable void detection Stellar Mass Calculation (default: true) - Enable mass index Orbital Cycles (default: true) - Enable cyclical visualization Supernova Detection (default: true) - Enable extreme event detection Suggested Use Cases Identify accumulation phases for potential long entries Watch for distribution phases as potential exit signals Use climax volume as potential exhaustion indicators Monitor volume flow for directional bias Avoid trading during black hole (low liquidity) periods Watch for supernova events as potential trend acceleration Timeframe Recommendations Best on 15m to Daily charts. Volume analysis requires sufficient trading activity for meaningful readings. Limitations Volume data quality varies by exchange and instrument Buy/sell separation is based on candle direction, not actual order flow Astrophysics concepts are analogies, not literal physics A/D phase detection may lag during rapid transitions Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
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Entropy Balance Oscillator [JOAT] Entropy Balance Oscillator - Chaos Theory Edition Overview Entropy Balance Oscillator is an open-source oscillator indicator that applies chaos theory concepts to market analysis. It calculates market entropy (disorder/randomness), balance (price position within range), and various chaos metrics to identify whether the market is in an ordered, chaotic, or balanced state. This helps traders understand market regime and adjust their strategies accordingly. What This Indicator Does The indicator calculates and displays: Entropy - Measures market disorder using return distribution analysis Balance - Price position within the high-low range, normalized to -1 to +1 Lyapunov Exponent - Estimates sensitivity to initial conditions (chaos indicator) Hurst Exponent - Measures long-term memory in price series (trend persistence) Strange Attractor - Simulated attractor points for visualization Bifurcation Detection - Identifies potential regime change points Chaos Index - Combined entropy and volatility score Market Phase - Classification as CHAOS, ORDER, or BALANCED How It Works Entropy is calculated using return distribution: calculateEntropy(series float price, simple int period) => // Calculate returns and their absolute values // Sum absolute returns for normalization // Apply Shannon entropy formula: -sum(p * log(p)) float entropy = 0.0 for i = 0 to array.size(returns) - 1 float prob = math.abs(array.get(returns, i)) / sumAbs if prob > 0 entropy -= prob * math.log(prob) entropy Balance measures price position within range: calculateBalance(series float high, series float low, series float close, simple int period) => float range = high - low float position = (close - low) / (range > 0 ? range : 1) float balance = ta.ema(position, period) (balance - 0.5) * 2 // Normalize to -1 to +1 Lyapunov Exponent estimates chaos sensitivity: lyapunovExponent(series float price, simple int period) => float sumLog = 0.0 for i = 1 to period float ratio = price > 0 ? math.abs(price / price ) : 1.0 if ratio > 0 sumLog += math.log(ratio) lyapunov := sumLog / period Hurst Exponent measures trend persistence: H > 0.5: Trending/persistent behavior H = 0.5: Random walk H < 0.5: Mean-reverting behavior Signal Generation Phase changes and extreme conditions generate signals: Chaos Phase: Normalized entropy exceeds chaos threshold (default 0.7) Order Phase: Normalized entropy falls below order threshold (default 0.3) Extreme Chaos: Entropy exceeds 1.5x chaos threshold Extreme Order: Entropy falls below 0.5x order threshold Bifurcation: Variance exceeds 2x average variance Dashboard Panel (Top-Right) Market Phase - Current phase (CHAOS/ORDER/BALANCED) Entropy Level - Normalized entropy value Balance - Current balance reading (-1 to +1) Chaos Index - Combined chaos score percentage Volatility - Current price volatility Lyapunov Exp - Lyapunov exponent value Hurst Exponent - Hurst exponent value Chaos Score - Overall chaos assessment Status - Current market status Visual Elements Entropy Line - Main oscillator showing normalized entropy Entropy EMA - Smoothed entropy for trend reference Balance Area - Filled area showing balance direction Chaos/Order Thresholds - Horizontal dashed lines Lyapunov Line - Step line showing Lyapunov exponent Strange Attractor - Circle plots showing attractor points Phase Space - Line showing phase space reconstruction Phase Background - Background color based on current phase Extreme Markers - X-cross for extreme chaos, diamond for extreme order Bifurcation Markers - Circles at potential regime changes Input Parameters Entropy Period (default: 20) - Period for entropy calculation Balance Period (default: 14) - Period for balance calculation Chaos Threshold (default: 0.7) - Threshold for chaos phase Order Threshold (default: 0.3) - Threshold for order phase Lyapunov Exponent (default: true) - Enable Lyapunov calculation Hurst Exponent (default: true) - Enable Hurst calculation Strange Attractor (default: true) - Enable attractor visualization Bifurcation Detection (default: true) - Enable bifurcation detection Suggested Use Cases Identify market regime for strategy selection (trend-following vs mean-reversion) Watch for phase changes as potential trading environment shifts Use Hurst exponent to assess trend persistence Monitor chaos index for volatility regime awareness Avoid trading during extreme chaos phases Timeframe Recommendations Best on 1H to Daily charts. Chaos metrics require sufficient data for meaningful calculations. Limitations Chaos theory concepts are applied as analogies, not rigorous mathematical implementations Lyapunov and Hurst calculations are simplified approximations Strange attractor visualization is conceptual Bifurcation detection uses variance as proxy Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
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Velocity Divergence Radar [JOAT] Velocity Divergence Radar - Momentum Physics Edition Overview Velocity Divergence Radar is an open-source oscillator indicator that applies physics concepts to market analysis. It calculates price velocity (rate of change), acceleration (rate of velocity change), and jerk (rate of acceleration change) to provide a multi-dimensional view of momentum. The indicator also includes divergence detection and force vector analysis. What This Indicator Does The indicator calculates and displays: Velocity - Rate of price change over a configurable period, smoothed with EMA Acceleration - Rate of velocity change, showing momentum shifts Jerk (3rd Derivative) - Rate of acceleration change, indicating momentum stability Force Vectors - Volume-weighted acceleration representing market force Kinetic Energy - Calculated as 0.5 * mass (volume ratio) * velocity squared Momentum Conservation - Tracks momentum relative to historical average Divergence Detection - Identifies when price and velocity diverge at pivots How It Works Velocity is calculated as smoothed rate of change: calculateVelocity(series float price, simple int period) => float roc = ta.roc(price, period) float velocity = ta.ema(roc, period / 2) velocity Acceleration is the change in velocity: calculateAcceleration(series float velocity, simple int period) => float accel = ta.change(velocity, period) float smoothAccel = ta.ema(accel, period / 2) smoothAccel Jerk is the change in acceleration: calculateJerk(series float acceleration, simple int period) => float jerk = ta.change(acceleration, period) float smoothJerk = ta.ema(jerk, period / 2) smoothJerk Force is calculated using F = m * a (mass approximated by volume ratio): calculateForceVector(series float mass, series float acceleration) => float force = mass * acceleration float forceDirection = math.sign(force) float forceMagnitude = math.abs(force) Signal Generation Signals are generated based on velocity behavior: Bullish Divergence: Price makes lower low while velocity makes higher low Bearish Divergence: Price makes higher high while velocity makes lower high Velocity Cross: Velocity crosses above/below zero line Extreme Velocity: Velocity exceeds 1.5x the upper/lower zone threshold Jerk Extreme: Jerk exceeds 2x standard deviation Force Extreme: Force magnitude exceeds 2x average Dashboard Panel (Top-Right) Velocity - Current velocity value Acceleration - Current acceleration value Momentum Strength - Combined velocity and acceleration strength Radar Score - Composite score based on velocity and acceleration Direction - STRONG UP/SLOWING UP/STRONG DOWN/SLOWING DOWN/FLAT Jerk - Current jerk value Force Vector - Current force magnitude Kinetic Energy - Current kinetic energy value Physics Score - Overall physics-based momentum score Signal - Current actionable status Visual Elements Velocity Line - Main oscillator line with color based on direction Velocity EMA - Smoothed velocity for trend reference Acceleration Histogram - Bar chart showing acceleration direction Jerk Area - Filled area showing jerk magnitude Vector Magnitude - Line showing combined vector strength Radar Scan - Oscillating pattern for visual effect Zone Lines - Upper and lower threshold lines Divergence Labels - BULL DIV / BEAR DIV markers Extreme Markers - Triangles at velocity extremes Input Parameters Velocity Period (default: 14) - Period for velocity calculation Acceleration Period (default: 7) - Period for acceleration calculation Divergence Lookback (default: 10) - Bars to scan for divergence Radar Sensitivity (default: 1.0) - Zone threshold multiplier Jerk Analysis (default: true) - Enable 3rd derivative calculation Force Vectors (default: true) - Enable force analysis Kinetic Energy (default: true) - Enable energy calculation Momentum Conservation (default: true) - Enable momentum tracking Suggested Use Cases Identify momentum direction using velocity sign and magnitude Watch for divergences as potential reversal warnings Use acceleration to detect momentum shifts before price confirms Monitor jerk for momentum stability assessment Combine force and kinetic energy for conviction analysis Timeframe Recommendations Works on all timeframes. Higher timeframes provide smoother readings; lower timeframes show more granular momentum changes. Limitations Physics analogies are conceptual and not literal market physics Divergence detection uses pivot-based lookback and may lag Force calculation uses volume ratio as mass proxy Kinetic energy is a derived metric, not actual energy Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
Pine Script® göstergesi
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Quality-Controlled Trend Strategy v2 (Expectancy Focused)This script focuses on quality control rather than curve-fitting. No repainting, no intrabar tricks, no fake equity curves. It uses confirmed-bar entries, ATR-based risk, and clean trend logic so backtests reflect what could actually be traded live. If you publish scripts, this is the minimum structure worth sharing. Why this script exists TradingView’s public scripts are flooded with: repainting indicators no stop-loss logic curve-fit entries that collapse live strategies that look good only in hindsight This script is intentionally boring but honest. No repainting. No intrabar tricks. No fake equity curves The goal is quality control, not hype. What this strategy enforces ✔ Confirmed bars only ✔ Single source of truth for indicators ✔ Fixed risk structure ✔ No signal repainting ✔ Clean exits with unique IDs ✔ Works on any liquid market Trading Logic (simple & auditable) Trend filter EMA 50 vs EMA 200 Entry Pullback to EMA 50 RSI confirms momentum (not oversold/overbought) Risk ATR-based stop Fixed R:R One position at a time This is the minimum bar for a strategy to be considered publish-worthy. Why this helps TradingView quality Most low-value scripts fail because they: hide repainting logic skip exits entirely use inconsistent calculations rely on hindsight candles This strategy forces discipline: every signal is confirmed every trade has defined risk behavior is repeatable across symbols & timeframes If more scripts followed this baseline, TradingView’s public library would be far more usable.
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Fractal Market Geometry [JOAT] Fractal Market Geometry Overview Fractal Market Geometry is an open-source overlay indicator that combines fractal analysis with harmonic pattern detection, Fibonacci retracements and extensions, Elliott Wave concepts, and Wyckoff phase identification. It provides traders with a geometric framework for understanding market structure and identifying potential reversal patterns with multi-factor signal confirmation. What This Indicator Does The indicator calculates and displays: Fractal Detection - Identifies fractal highs and lows using Williams-style pivot analysis with configurable period Fractal Dimension - Calculates market complexity using range-based dimension estimation Harmonic Patterns - Detects Gartley, Butterfly, Bat, Crab, Shark, Cypher, and ABCD patterns using Fibonacci ratios Fibonacci Retracements - Key levels at 38.2%, 50%, and 61.8% Fibonacci Extensions - Projection level at 161.8% Elliott Wave Count - Simplified wave counting based on pivot detection (1-5) Wyckoff Phase - Volume-based phase identification (Accumulation, Markup, Distribution, Neutral) Golden Spiral Levels - ATR-based support and resistance levels using phi (1.618) ratio Trend Detection - EMA crossover trend identification (20/50 EMA) How It Works Fractal detection uses a configurable period to identify swing points: detectFractalHigh(simple int period) => bool result = true float centerVal = high for i = 0 to period - 1 if high >= centerVal or high >= centerVal result := false break Harmonic pattern detection uses Fibonacci ratio analysis between swing points. Each pattern has specific ratio requirements: Gartley: AB 0.382-0.618, BC 0.382-0.886, CD 1.27-1.618 Butterfly: AB 0.382-0.5, BC 0.382-0.886, CD 1.618-2.24 Bat: AB 0.5-0.618, BC 1.13-1.618, CD 1.618-2.24 Crab: AB 0.382-0.618, BC 0.382-0.886, CD 2.24-3.618 Shark: AB 0.382-0.618, BC 1.13-1.618, CD 1.618-2.24 Cypher: AB 0.382-0.618, BC 1.13-1.414, CD 0.786-0.886 Wyckoff phase detection analyzes volume relative to price movement: wyckoffPhase(simple int period) => float avgVol = ta.sma(volume, period) float priceChg = ta.change(close, period) string phase = "NEUTRAL" if volume > avgVol * 1.5 and math.abs(priceChg) < close * 0.02 phase := "ACCUMULATION" else if volume > avgVol * 1.5 and math.abs(priceChg) > close * 0.05 phase := "MARKUP" else if volume < avgVol * 0.7 phase := "DISTRIBUTION" phase Signal Generation Signals use multi-factor confirmation for accuracy: BUY Signal: Fractal low + Uptrend (EMA20 > EMA50) + RSI 30-55 + Bullish candle + Volume confirmation SELL Signal: Fractal high + Downtrend (EMA20 < EMA50) + RSI 45-70 + Bearish candle + Volume confirmation Pattern Detection: Label appears when harmonic pattern completes at current bar Dashboard Panel (Top-Right) Dimension - Fractal dimension value (market complexity measure) Last High - Most recent fractal high price Last Low - Most recent fractal low price Pattern - Current harmonic pattern name or NONE Elliott Wave - Current wave count (Wave 1-5) or OFF Wyckoff - Current market phase or OFF Trend - BULLISH, BEARISH, or NEUTRAL based on EMA crossover Signal - BUY, SELL, or WAIT status Visual Elements Fractal Markers - Small triangles at fractal highs (down arrow) and lows (up arrow) Geometry Lines - Dashed lines connecting the most recent fractal high and low Fibonacci Levels - Clean horizontal lines at 38.2%, 50%, and 61.8% retracement levels Fibonacci Extension - Horizontal line at 161.8% extension level Golden Spiral Levels - Support and resistance lines based on ATR x 1.618 3D Fractal Field - Optional depth layers around swing levels (OFF by default) Harmonic Pattern Markers - Small diamond shapes when Crab, Shark, or Cypher patterns detected Pattern Labels - Text label showing pattern name when detected Signal Labels - BUY/SELL labels on confirmed multi-factor signals Input Parameters Fractal Period (default: 5) - Bars on each side for fractal detection Geometry Depth (default: 3) - Complexity of geometric calculations Pattern Sensitivity (default: 0.8) - Tolerance for pattern ratio matching Show Fibonacci Levels (default: true) - Display retracement levels Show Fibonacci Extensions (default: true) - Display extension level Elliott Wave Detection (default: true) - Enable wave counting Wyckoff Analysis (default: true) - Enable phase detection Golden Spiral Levels (default: true) - Display spiral support/resistance Show Fractal Points (default: true) - Display fractal markers Show Geometry Lines (default: true) - Display connecting lines Show Pattern Labels (default: true) - Display pattern name labels Show 3D Fractal Field (default: false) - Display depth layers Show Harmonic Patterns (default: true) - Display pattern markers Show Buy/Sell Signals (default: true) - Display signal labels Suggested Use Cases Identify potential reversal zones using harmonic pattern completion Use Fibonacci levels for entry, stop-loss, and target planning Monitor Wyckoff phases for accumulation/distribution awareness Track Elliott Wave counts for trend structure analysis Use fractal dimension to gauge market complexity Wait for multi-factor signal confirmation before entering trades Timeframe Recommendations Best on 1H to Daily charts. Lower timeframes produce more fractals but with less significance. Higher timeframes provide stronger levels and more reliable signals. Limitations Harmonic pattern detection uses simplified ratio ranges and may not match all textbook definitions Elliott Wave counting is basic and does not include all wave rules Wyckoff phase detection is volume-based approximation Fractal dimension calculation is simplified Signals require fractal confirmation which has inherent lag equal to the fractal period Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management. - Made with passion by officialjackofalltrades
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Dimensional Support ResistanceDimensional Support Resistance Overview Dimensional Support Resistance is an open-source overlay indicator that automatically detects and displays clean, non-overlapping support and resistance levels using pivot-based analysis with intelligent filtering. It identifies significant swing highs and lows, filters them by minimum distance to prevent visual clutter, and provides volume-confirmed bounce signals. What This Indicator Does The indicator calculates and displays: Dynamic Pivot Levels - Automatically detected swing highs and lows based on configurable pivot strength Distance Filtering - Ensures levels are spaced apart by a minimum percentage to prevent overlap S/R Zones - Visual zones around each level showing the price area of significance Bounce Detection - Identifies when price reverses at support or resistance levels Volume Confirmation - Strong signals require above-average volume for confirmation How It Works Pivot detection scans for swing highs and lows using a configurable strength parameter. A pivot low requires the low to be lower than all surrounding bars within the strength period. Signal Generation The indicator generates bounce signals using TradingView's built-in pivot detection combined with candle reversal confirmation: Support Bounce: Pivot low forms with bullish close (close > open) Resistance Bounce: Pivot high forms with bearish close (close < open) Strong Bounce: Bounce occurs with volume 1.5x above 20-period average A cooldown period of 15 bars prevents signal spam. Dashboard Panel A compact dashboard displays: Support - Count of active support levels Resistance - Count of active resistance levels Dashboard position is configurable (Top Left, Top Right, Bottom Left, Bottom Right). Visual Elements Support Lines - Green horizontal lines at support levels Resistance Lines - Red horizontal lines at resistance levels S/R Zones - Semi-transparent boxes around levels showing zone width Price Labels - S: and R: labels showing exact price of nearest levels BOUNCE Markers - Triangle shapes with text when price bounces at a level STRONG Markers - Label shapes when bounce occurs with high volume Input Parameters Lookback Period (default: 100) - Historical bars to scan for pivots Pivot Strength (default: 8) - Bars on each side required for valid pivot (higher = fewer but stronger levels) Max Levels Each Side (default: 2) - Maximum support and resistance levels displayed Zone Width % (default: 0.15) - Width of zones around each level as percentage of price Min Distance Between Levels % (default: 1.0) - Minimum spacing between levels to prevent overlap Show S/R Zones (default: true) - Toggle zone visualization Show Bounce Signals (default: true) - Toggle signal markers Support Color (default: #00ff88) - Color for support elements Resistance Color (default: #ff3366) - Color for resistance elements Suggested Use Cases Identify key support and resistance levels for entry and exit planning Use bounce signals as potential reversal confirmation Combine with other indicators for confluence-based trading decisions Monitor strong signals for high-probability setups with volume confirmation Timeframe Recommendations Works on all timeframes. Higher timeframes (4H, Daily) provide more significant levels with fewer signals. Lower timeframes show more granular structure but may produce more noise. Limitations Pivot detection requires lookback bars, so very recent pivots may not be immediately visible Bounce signals are based on pivot formation and may lag by the pivot strength period Levels are recalculated on each bar, so they may shift as new pivots form Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management and conduct your own analysis before trading. - Made with passion by officialjackofalltrades
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Ocean Master [JOAT]Ocean Master QE - Advanced Oceanic Market Analysis with Quantum Flow Dynamics Overview Ocean Master QE is an open-source overlay indicator that combines multiple analytical techniques into a unified market analysis framework. It uses ATR-based dynamic channels, volume-weighted order flow analysis, multi-timeframe correlation (quantum entanglement concept), and harmonic oscillator calculations to provide traders with a comprehensive view of market conditions. What This Indicator Does The indicator calculates and displays several key components: Dynamic Price Channels - ATR-adjusted upper, middle, and lower channels that adapt to current volatility conditions Order Flow Analysis - Separates buying and selling volume pressure to calculate a directional delta Smart Money Index - Volume-weighted order flow metric that highlights potential institutional activity Harmonic Oscillator - Weighted combination of 10 Fibonacci-period EMAs (5, 8, 13, 21, 34, 55, 89, 144, 233, 377) to identify trend direction Multi-Timeframe Correlation - Measures price correlation across 1H, 4H, and Daily timeframes Wave Function Analysis - Momentum-based state detection that identifies when price action becomes decisive How It Works The core channel calculation uses ATR with a configurable quantum sensitivity factor: float atr = ta.atr(i_atrLength) float quantumFactor = 1.0 + (i_quantumSensitivity * 0.1) float quantumATR = atr * quantumFactor upperChannel := ta.highest(high, i_length) - (quantumATR * 0.5) lowerChannel := ta.lowest(low, i_length) + (quantumATR * 0.5) midChannel := (upperChannel + lowerChannel) * 0.5 Order flow is calculated by separating volume into buy and sell components based on candle direction: The harmonic oscillator weights shorter EMAs more heavily using inverse weighting (1/1, 1/2, 1/3... 1/10), creating a responsive yet smooth trend indicator. Signal Generation Confluence signals require multiple conditions to align: Bullish: Harmonic oscillator crosses above zero + positive Smart Money Index + positive Order Flow Delta Bearish: Harmonic oscillator crosses below zero + negative Smart Money Index + negative Order Flow Delta Dashboard Panel (Top-Right) Bias - Current market direction based on price vs mid-channel Entanglement - Multi-timeframe correlation score (0-100%) Wave State - COLLAPSED (decisive) or SUPERPOSITION (uncertain) Volume - Current volume relative to 20-period average Volatility - ATR as percentage of price Smart Money - Volume-weighted order flow reading Visual Elements Ocean Depth Layers - Gradient fills between channel levels representing different price zones Channel Lines - Upper (surface), middle, and lower (seabed) dynamic levels Divergence Markers - Triangle shapes when harmonic oscillator crosses zero Confluence Labels - BULL/BEAR labels when multiple factors align Suggested Use Cases Identify trend direction using the harmonic oscillator and channel position Monitor order flow for potential institutional activity Use multi-timeframe correlation to confirm trade direction across timeframes Watch for confluence signals where multiple factors align Input Parameters Length (default: 14) - Base period for channel and indicator calculations ATR Length (default: 14) - Period for ATR calculation Quantum Depth (default: 3) - Complexity factor for calculations Quantum Sensitivity (default: 1.5) - Channel width multiplier Timeframe Recommendations Works on all timeframes. Higher timeframes (4H, Daily) provide smoother signals; lower timeframes require faster reaction times and may produce more noise. Limitations Multi-timeframe requests add processing overhead Order flow estimation is based on candle direction, not actual order book data Correlation calculations require sufficient historical data Open-Source and Disclaimer This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management and conduct your own analysis before trading. - Made with passion by officialjackofalltrades
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Session Volume Analyzer [JOAT] Session Volume Analyzer — Global Trading Session and Volume Intelligence System This indicator addresses the analytical challenge of understanding market participation patterns across global trading sessions. It combines precise session detection with comprehensive volume analysis to provide insights into when and how different market participants are active. The tool recognizes that different trading sessions exhibit distinct characteristics in terms of participation, volatility, and volume patterns. Why This Combination Provides Unique Analytical Value Traditional session indicators typically only show time boundaries, while volume indicators show raw volume data without session context. This creates analytical gaps: 1. **Session Context Missing**: Volume spikes without session context provide incomplete information 2. **Participation Patterns Hidden**: Different sessions have different participant types (retail, institutional, algorithmic) 3. **Comparative Analysis Lacking**: No easy way to compare volume patterns across sessions 4. **Timing Intelligence Absent**: Understanding WHEN volume occurs is as important as HOW MUCH volume occurs This indicator's originality lies in creating an integrated session-volume analysis system that: **Provides Session-Aware Volume Analysis**: Volume data is contextualized within specific trading sessions **Enables Cross-Session Comparison**: Compare volume patterns between Asian, London, and New York sessions **Delivers Participation Intelligence**: Understand which sessions are showing above-normal participation **Offers Real-Time Session Tracking**: Know exactly which session is active and how current volume compares Technical Innovation and Originality While session detection and volume analysis exist separately, the innovation lies in: 1. **Integrated Session-Volume Architecture**: Simultaneous tracking of session boundaries and volume statistics creates comprehensive market participation analysis 2. **Multi-Session Volume Comparison System**: Real-time calculation and comparison of volume statistics across different global sessions 3. **Adaptive Volume Threshold Detection**: Automatic identification of above-average volume periods within session context 4. **Comprehensive Visual Integration**: Session backgrounds, volume highlights, and statistical dashboards provide complete market participation picture How Session Detection and Volume Analysis Work Together The integration creates a sophisticated market participation analysis system: **Session Detection Logic**: Uses Pine Script's time functions to identify active sessions // Session detection based on exchange time bool inAsian = not na(time(timeframe.period, asianSession)) bool inLondon = not na(time(timeframe.period, londonSession)) bool inNY = not na(time(timeframe.period, nySession)) // Session transition detection bool asianStart = inAsian and not inAsian bool londonStart = inLondon and not inLondon bool nyStart = inNY and not inNY **Volume Analysis Integration**: Volume statistics are calculated within session context // Session-specific volume accumulation if asianStart asianVol := 0.0 asianBars := 0 if inAsian asianVol += volume asianBars += 1 // Real-time session volume analysis float asianAvgVol = asianBars > 0 ? asianVol / asianBars : 0 **Relative Volume Assessment**: Current volume compared to session-specific averages float volMA = ta.sma(volume, volLength) float volRatio = volMA > 0 ? volume / volMA : 1 // Volume classification within session context bool isHighVol = volRatio >= 1.5 and volRatio < 2.5 bool isVeryHighVol = volRatio >= 2.5 This creates a system where volume analysis is always contextualized within the appropriate trading session, providing more meaningful insights than raw volume data alone. Comprehensive Session Analysis Framework **Default Session Definitions** (customizable based on broker timezone): - **Asian Session**: 1800-0300 (exchange time) - Represents Asian market participation including Tokyo, Hong Kong, Singapore - **London Session**: 0300-1200 (exchange time) - Represents European market participation - **New York Session**: 0800-1700 (exchange time) - Represents North American market participation **Session Overlap Analysis**: The system recognizes and highlights overlap periods: - **London/New York Overlap**: 0800-1200 - Typically the highest volume period - **Asian/London Overlap**: 0300-0300 (brief) - Transition period - **New York/Asian Overlap**: 1700-1800 (brief) - End of NY, start of Asian **Volume Intelligence Features**: 1. **Session-Specific Volume Accumulation**: Tracks total volume within each session 2. **Cross-Session Volume Comparison**: Compare current session volume to other sessions 3. **Relative Volume Detection**: Identify when current volume exceeds historical averages 4. **Participation Pattern Analysis**: Understand which sessions show consistent high/low participation Advanced Volume Analysis Methods **Relative Volume Calculation**: float volMA = ta.sma(volume, volLength) // Volume moving average float volRatio = volMA > 0 ? volume / volMA : 1 // Current vs average ratio // Multi-tier volume classification bool isNormalVol = volRatio < 1.5 bool isHighVol = volRatio >= 1.5 and volRatio < 2.5 bool isVeryHighVol = volRatio >= 2.5 bool isExtremeVol = volRatio >= 4.0 **Session Volume Tracking**: // Cumulative session volume with bar counting if londonStart londonVol := 0.0 londonBars := 0 if inLondon londonVol += volume londonBars += 1 // Average volume per bar calculation float londonAvgVol = londonBars > 0 ? londonVol / londonBars : 0 **Cross-Session Volume Comparison**: The system maintains running totals for each session, enabling real-time comparison of participation levels across different global markets. What the Display Shows Session Backgrounds — Colored backgrounds indicating which session is active - Pink: Asian session - Blue: London session - Green: New York session Session Open Lines — Horizontal lines at each session's opening price Session Markers — Labels (AS, LN, NY) when sessions begin Volume Highlights — Bar coloring when volume exceeds thresholds - Orange: High volume (1.5x+ average) - Red: Very high volume (2.5x+ average) Dashboard — Current session, cumulative volume, and averages Color Scheme Asian — #E91E63 (pink) London — #2196F3 (blue) New York — #4CAF50 (green) High Volume — #FF9800 (orange) Very High Volume — #F44336 (red) Inputs Session Times: Asian Session window (default: 1800-0300) London Session window (default: 0300-1200) New York Session window (default: 0800-1700) Volume Settings: Volume MA Length (default: 20) High Volume threshold (default: 1.5x) Very High Volume threshold (default: 2.5x) Visual Settings: Session colors (customizable) Show/hide backgrounds, lines, markers Background transparency How to Read the Display Background color shows which session is currently active Session open lines show where each session started Orange/red bars indicate above-average volume Dashboard shows cumulative volume for each session today Alerts Session opened (Asian, London, New York) High volume bar detected Very high volume bar detected Important Limitations and Realistic Expectations Session times are approximate and depend on your broker's server timezone—manual adjustment may be required for accuracy Volume data quality varies significantly by broker, instrument, and market type Cryptocurrency and some forex markets trade continuously, making traditional session boundaries less meaningful High volume indicates participation level only—it does not predict price direction or market outcomes Session participation patterns can change over time due to market structure evolution, holidays, and economic conditions This tool displays historical and current market participation data—it cannot predict future volume or price movements Volume spikes can occur for numerous reasons unrelated to directional price movement (news, algorithmic trading, etc.) Different instruments exhibit different session sensitivity and volume patterns Market holidays and special events can significantly alter normal session patterns Appropriate Use Cases This indicator is designed for: - Market participation pattern analysis - Session-based trading schedule planning - Volume context and comparison across sessions - Educational study of global market structure - Supplementary analysis for session-based strategies This indicator is NOT designed for: - Standalone trading signal generation - Volume-based price direction prediction - Automated trading system triggers - Guaranteed session pattern repetition - Replacement of fundamental or sentiment analysis Understanding Session Analysis Limitations Session analysis provides valuable context but has inherent limitations: - Session patterns can change due to economic conditions, holidays, and market structure evolution - Volume patterns may not repeat consistently across different market conditions - Global events can override normal session characteristics - Different asset classes respond differently to session boundaries - Technology and algorithmic trading continue to blur traditional session distinctions — Made with passion by officialjackofalltrades
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Volatility Squeeze Pro [JOAT] Volatility Squeeze Pro — Advanced Volatility Compression Analysis System This indicator addresses a specific analytical challenge in volatility analysis: how to identify periods when different volatility measurements show compression relationships that may indicate potential energy buildup in the market. It combines two distinct volatility calculation methods—standard deviation-based bands and ATR-based channels—with a momentum oscillator to provide comprehensive volatility state analysis. Why This Combination Provides Unique Analytical Value Traditional volatility indicators typically focus on single measurements, but markets exhibit different types of volatility that require different analytical approaches: 1. **Closing Price Volatility** (Standard Deviation): Measures how much closing prices deviate from their average 2. **Trading Range Volatility** (ATR): Measures the actual high-to-low trading ranges 3. **Directional Momentum**: Measures where price sits within its recent range The problem with using these individually: - Standard deviation alone doesn't account for intraday volatility - ATR alone doesn't consider closing price clustering - Momentum alone doesn't provide volatility context - No single measurement captures the complete volatility picture This indicator's originality lies in creating a comprehensive volatility analysis system that: **Identifies Volatility Compression**: When closing price volatility contracts inside trading range volatility, it suggests potential energy buildup **Provides Momentum Context**: Shows directional bias during compression periods **Offers Multi-Dimensional Analysis**: Combines three different analytical approaches into one coherent system **Delivers Real-Time Assessment**: Continuously monitors the relationship between different volatility types Technical Innovation and Originality While individual components (Bollinger Bands, Keltner Channels, Linear Regression) are standard, the innovation lies in: 1. **Volatility Relationship Detection**: The mathematical comparison between standard deviation bands and ATR channels creates a unique compression identification system 2. **Integrated Momentum Analysis**: Linear regression-based momentum calculation provides directional context specifically during volatility compression periods 3. **Multi-State Visualization**: The indicator provides clear visual encoding of different volatility states (compressed vs. normal) with momentum direction 4. **Adaptive Threshold System**: The squeeze detection automatically adapts to different instruments and timeframes without manual calibration How the Components Work Together Analytically The three components create a comprehensive volatility analysis framework: **Standard Deviation Component**: Measures closing price dispersion around the mean float bbBasis = ta.sma(close, bbLength) float bbDev = bbMult * ta.stdev(close, bbLength) float bbUpper = bbBasis + bbDev float bbLower = bbBasis - bbDev **ATR Channel Component**: Measures actual trading range volatility float kcBasis = ta.ema(close, kcLength) float kcRange = ta.atr(atrLength) float kcUpper = kcBasis + kcRange * kcMult float kcLower = kcBasis - kcRange * kcMult **Squeeze Detection Logic**: Identifies when closing price volatility compresses within trading range volatility bool squeezeOn = bbLower > kcLower and bbUpper < kcUpper // This condition indicates closing prices are clustering more tightly // than the typical trading range would suggest **Momentum Context Component**: Provides directional bias during compression float highestHigh = ta.highest(high, momLength) float lowestLow = ta.lowest(low, momLength) float momentum = ta.linreg(close - math.avg(highestHigh, lowestLow), momLength, 0) float momSmooth = ta.sma(momentum, smoothLength) The analytical relationship creates a system where: - Squeeze detection identifies WHEN volatility compression occurs - Momentum analysis shows WHERE price is positioned during compression - Combined analysis provides both timing and directional context How the Volatility Comparison Works The indicator compares two volatility measurements: Standard Deviation Bands These measure how much closing prices deviate from their average. When prices cluster tightly around the average, the bands contract. // Standard deviation bands calculation float bbBasis = ta.sma(close, bbLength) float bbDev = bbMult * ta.stdev(close, bbLength) float bbUpper = bbBasis + bbDev float bbLower = bbBasis - bbDev ATR-Based Channels These measure volatility using Average True Range—the typical distance between high and low prices. They respond to the actual trading range rather than closing price dispersion. // ATR-based channels calculation float kcBasis = ta.ema(close, kcLength) float kcRange = ta.atr(atrLength) float kcUpper = kcBasis + kcRange * kcMult float kcLower = kcBasis - kcRange * kcMult The Squeeze Condition A "squeeze" is detected when the standard deviation bands are completely contained within the ATR channels: // Squeeze detection bool squeezeOn = bbLower > kcLower and bbUpper < kcUpper This condition indicates that closing price volatility has compressed relative to the overall trading range. The Momentum Component The momentum oscillator measures where price sits relative to its recent high-low range, using linear regression for smoothing: // Momentum calculation float highestHigh = ta.highest(high, momLength) float lowestLow = ta.lowest(low, momLength) float momentum = ta.linreg(close - math.avg(highestHigh, lowestLow), momLength, 0) float momSmooth = ta.sma(momentum, smoothLength) Positive values indicate price is above the midpoint of its recent range; negative values indicate below. Why Display Both Together The squeeze detection shows WHEN volatility is compressed. The momentum reading shows the current directional bias of price within that compression. Together, they provide two pieces of information: 1. Is volatility currently compressed? (squeeze status) 2. Where is price leaning within the current range? (momentum) These are observations about current conditions, not predictions about future movement. Visual Elements Momentum Histogram — Bars showing momentum value - Green shades: Positive momentum (price above range midpoint) - Red shades: Negative momentum (price below range midpoint) - Brighter colors: Momentum increasing - Faded colors: Momentum decreasing Squeeze Dots — Circles on the zero line - Red: Squeeze condition active - Green: No squeeze condition Release Markers — Triangle markers when squeeze condition ends Dashboard — Current readings and status Color Scheme Squeeze Active — #FF5252 (red) No Squeeze — #4CAF50 (green) Momentum Positive — #00E676 / #81C784 (green shades) Momentum Negative — #FF5252 / #E57373 (red shades) Inputs Standard Deviation Bands: Length (default: 20) Multiplier (default: 2.0) ATR Channels: Length (default: 20) Multiplier (default: 1.5) ATR Period (default: 10) Momentum: Length (default: 12) Smoothing (default: 3) How to Read the Display Red dots indicate the squeeze condition is present Green dots indicate normal volatility relationship Histogram direction shows current momentum bias Histogram color brightness shows whether momentum is increasing or decreasing Alerts Squeeze condition started Squeeze condition ended Squeeze ended with positive momentum Squeeze ended with negative momentum Extended squeeze (8+ bars) Important Limitations and Realistic Expectations Volatility compression detection is a mathematical relationship between calculations—it does not predict future price movements Many compression periods do not result in significant price expansion or directional moves Momentum direction during compression does not reliably indicate future breakout direction This indicator analyzes current and historical volatility conditions only—it cannot predict future volatility False signals are common—not every squeeze leads to tradeable price movement Different parameter settings will produce different compression detection sensitivity Market conditions, news events, and fundamental factors often override technical volatility patterns No volatility indicator can predict the timing, direction, or magnitude of future price movements This tool should be used as one component of comprehensive market analysis Appropriate Use Cases This indicator is designed for: - Volatility state analysis and monitoring - Educational study of volatility relationships - Multi-dimensional volatility assessment - Supplementary analysis alongside other technical tools - Understanding market compression/expansion cycles This indicator is NOT designed for: - Standalone trading signal generation - Guaranteed breakout prediction - Automated trading system triggers - Market timing precision - Replacement of fundamental analysis Understanding Volatility Analysis Limitations Volatility analysis, while useful for understanding market conditions, has inherent limitations: - Past volatility patterns do not guarantee future patterns - Compression periods can extend much longer than expected - Expansion periods may be brief and insufficient for trading - External factors (news, fundamentals) often override technical patterns - Different markets and timeframes exhibit different volatility characteristics — Made with passion by officialjackofalltrades
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Pivot Point Zones [JOAT]Pivot Point Zones — Multi-Formula Pivot Levels with ATR Zones Pivot Point Zones calculates and displays traditional pivot points with five formula options, enhanced with ATR-based zones around each level. This creates more practical trading zones that account for price noise around key levels—because price rarely reacts at exact mathematical levels. What Makes This Indicator Unique Unlike basic pivot point indicators, Pivot Point Zones: Offers five different pivot calculation formulas in one indicator Creates ATR-based zones around each level for realistic reaction areas Pulls data from higher timeframes automatically Displays clean labels with exact price values Provides a comprehensive dashboard with all levels What This Indicator Does Calculates pivot points using Standard, Fibonacci, Camarilla, Woodie, and more formulas Draws horizontal lines at Pivot, R1-R3, and S1-S3 levels Creates ATR-based zones around each level for realistic price reaction areas Displays labels with exact price values Updates automatically based on higher timeframe closes Provides fills between zone boundaries for visual clarity Pivot Formulas Explained // Standard Pivot - Classic (H+L+C)/3 calculation pp := (pivotHigh + pivotLow + pivotClose) / 3 r1 := 2 * pp - pivotLow s1 := 2 * pp - pivotHigh r2 := pp + pivotRange s2 := pp - pivotRange // Fibonacci Pivot - Uses Fib ratios for level spacing r1 := pp + 0.382 * pivotRange r2 := pp + 0.618 * pivotRange r3 := pp + 1.0 * pivotRange // Camarilla Pivot - Tighter levels for intraday r1 := pivotClose + pivotRange * 1.1 / 12 r2 := pivotClose + pivotRange * 1.1 / 6 r3 := pivotClose + pivotRange * 1.1 / 4 // Woodie Pivot - Weights current close more heavily pp := (pivotHigh + pivotLow + 2 * close) / 4 // TD Pivot - Conditional based on open/close relationship x = pivotClose < pivotOpen ? pivotHigh + 2*pivotLow + pivotClose : pivotClose > pivotOpen ? 2*pivotHigh + pivotLow + pivotClose : pivotHigh + pivotLow + 2*pivotClose pp := x / 4 Formula Characteristics Standard — Classic pivot calculation. Balanced levels, good for swing trading. Fibonacci — Uses 0.382, 0.618, and 1.0 ratios. Popular with Fibonacci traders. Camarilla — Tighter levels derived from range. Excellent for intraday mean-reversion. Woodie — Weights current close more heavily. More responsive to recent price action. TD — Conditional calculation based on open/close relationship. Adapts to bar type. Zone System Each pivot level includes an ATR-based zone that provides a more realistic area for potential price reactions: // ATR-based zone width calculation float atr = ta.atr(atrLength) float zoneHalf = atr * zoneWidth / 2 // Zone boundaries around each level zoneUpper = level + zoneHalf zoneLower = level - zoneHalf This accounts for market noise and helps avoid false breakout signals at exact level prices. Visual Features Pivot Lines — Horizontal lines at each calculated level Zone Fills — Transparent fills between zone boundaries Level Labels — Labels showing level name and exact price (e.g., "PP 45123.50") Color Coding : - Yellow: Pivot Point (PP) - Red gradient: Resistance levels (R1, R2, R3) - darker = further from PP - Green gradient: Support levels (S1, S2, S3) - darker = further from PP Color Scheme Pivot Color — Default: #FFEB3B (yellow) — Central pivot point Resistance Color — Default: #FF5252 (red) — R1, R2, R3 levels Support Color — Default: #4CAF50 (green) — S1, S2, S3 levels Zone Transparency — 85-90% transparent fills around levels Dashboard Information The on-chart table (bottom-right corner) displays: Selected pivot type (Standard, Fibonacci, etc.) R3, R2, R1 resistance levels with exact prices PP (Pivot Point) highlighted S1, S2, S3 support levels with exact prices Inputs Overview Pivot Settings: Pivot Type — Formula selection (Standard, Fibonacci, Camarilla, Woodie, TD) Pivot Timeframe — Higher timeframe for OHLC data (default: D = Daily) ATR Length — Period for zone width calculation (default: 14) Zone Width — ATR multiplier for zone size (default: 0.5) Level Display: Show Pivot (P) — Toggle central pivot line Show R1/S1 — Toggle first resistance/support levels Show R2/S2 — Toggle second resistance/support levels Show R3/S3 — Toggle third resistance/support levels Show Zones — Toggle ATR-based zone fills Show Labels — Toggle price labels at each level Visual Settings: Pivot/Resistance/Support Colors — Customizable color scheme Line Width — Thickness of level lines (default: 2) Extend Lines Right — Project lines forward on chart Show Dashboard — Toggle the information table How to Use It For Intraday Trading: Use Daily pivots on intraday charts (15m, 1H) Pivot point often acts as the day's "fair value" reference Camarilla levels work well for intraday mean-reversion R1/S1 are the most commonly tested levels For Swing Trading: Use Weekly pivots on daily charts Standard or Fibonacci formulas work well R2/S2 and R3/S3 become more relevant Zone boundaries provide realistic entry/exit areas For Support/Resistance: R levels above price act as resistance targets S levels below price act as support targets Zone boundaries are more realistic than exact lines Multiple formula confluence adds significance Alerts Available DPZ Cross Above Pivot — Price crosses above central pivot DPZ Cross Below Pivot — Price crosses below central pivot DPZ Cross Above R1/R2 — Price breaks resistance levels DPZ Cross Below S1/S2 — Price breaks support levels Best Practices Match pivot timeframe to your trading style (Daily for intraday, Weekly for swing) Use zones instead of exact levels for more realistic expectations Camarilla is best for mean-reversion; Standard/Fibonacci for breakouts Combine with other indicators for confirmation — Made with passion by officialjackofalltrades
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Smart Money Fluid [JOAT] Smart Money Fluid — Accumulation and Distribution Flow Analysis Smart Money Fluid tracks institutional-style accumulation and distribution patterns using a sophisticated combination of Money Flow Index, Chaikin Money Flow, and VWAP-relative price analysis. It aims to reveal whether larger participants may be accumulating (buying) or distributing (selling)—information that can precede significant price moves. What Makes This Indicator Unique Unlike single money flow indicators, Smart Money Fluid: Combines three different money flow methodologies into one composite signal Detects divergences between price and money flow automatically Identifies high-volume conditions that add conviction to signals Provides both the composite signal and individual component values Features a momentum histogram showing flow acceleration What This Indicator Does Combines multiple money flow indicators into a composite signal (0-100 scale) Identifies accumulation zones (potential institutional buying) and distribution zones (potential selling) Detects divergences between price and money flow Highlights high-volume conditions for stronger signals Tracks momentum direction within the flow Provides comprehensive dashboard with all component values Composite Calculation Explained The Smart Money Flow composite combines three proven money flow methodologies: // Component 1: Money Flow Index (MFI) - 40% weight // Measures buying/selling pressure using price and volume float mfi = 100 - (100 / (1 + mfRatio)) // Component 2: Chaikin Money Flow (CMF) - 30% weight // Measures accumulation/distribution based on close position within range float cmf = sum(mfVolume, length) / sum(volume, length) * 100 // Component 3: VWAP Price Strength - 30% weight // Measures price position relative to volume-weighted average price float priceVsVWAP = (close - vwap) / vwap * 100 // Final Composite (scaled to 0-100) float rawSMF = (mfi * 0.4 + (cmf + 50) * 0.3 + (50 + priceVsVWAP * 5) * 0.3) float smf = ta.ema(rawSMF, smoothLength) State Classification Accumulating (Green Zone) — SMF above accumulation threshold (default: 60). Suggests institutional buying may be occurring. Distributing (Red Zone) — SMF below distribution threshold (default: 40). Suggests institutional selling may be occurring. Neutral (Gray Zone) — SMF between thresholds. No clear accumulation or distribution detected. Divergence Detection The indicator automatically detects divergences using pivot analysis: Bullish Divergence — Price makes a lower low while SMF makes a higher low. This suggests selling pressure is weakening despite lower prices—potential reversal signal. Bearish Divergence — Price makes a higher high while SMF makes a lower high. This suggests buying pressure is weakening despite higher prices—potential reversal signal. Divergences are marked with "DIV" labels on the chart. Visual Features SMF Line with Glow — Main composite line with gradient coloring and glow effect Signal Line — Slower EMA of SMF for crossover signals Flow Momentum Histogram — Shows the difference between SMF and signal line with four-color coding: - Bright green: Positive and accelerating - Faded green: Positive but decelerating - Bright red: Negative and accelerating - Faded red: Negative but decelerating Zone Backgrounds — Green tint in accumulation zone, red tint in distribution zone Reference Lines — Dashed lines at accumulation/distribution thresholds, dotted line at 50 Strong Signal Markers — Triangles appear when accumulation/distribution occurs with high volume Divergence Labels — "DIV" markers when divergences are detected Color Scheme Accumulation Color — Default: #00E676 (bright green) Distribution Color — Default: #FF5252 (red) Neutral Color — Default: #9E9E9E (gray) Gradient Coloring — SMF line transitions smoothly between colors based on value Dashboard Information The on-chart table (top-right corner) displays: Current SMF value with state coloring State classification (ACCUMULATING, DISTRIBUTING, or NEUTRAL) Flow momentum direction (Up/Down with magnitude) MFI component value CMF component value with directional coloring Volume status (High or Normal) Active divergence detection (Bullish, Bearish, or None) Inputs Overview Calculation Settings: Money Flow Length — Period for flow calculations (default: 14, range: 5-50) Smoothing Length — EMA smoothing period (default: 5, range: 1-20) Divergence Lookback — Bars for pivot detection in divergence analysis (default: 5, range: 2-20) Sensitivity: Accumulation Threshold — Level above which accumulation is detected (default: 60, range: 50-90) Distribution Threshold — Level below which distribution is detected (default: 40, range: 10-50) High Volume Multiplier — Multiple of average volume for "high volume" classification (default: 1.5x, range: 1.0-3.0) Visual Settings: Accumulation/Distribution/Neutral Colors — Customizable color scheme Show Flow Histogram — Toggle momentum histogram Show Divergences — Toggle divergence detection and labels Show Dashboard — Toggle the information table Show Zone Background — Toggle colored backgrounds in accumulation/distribution zones Alerts: Await Bar Confirmation — Wait for bar close before triggering (recommended) How to Use It For Trend Confirmation: Accumulation during uptrends confirms buying pressure Distribution during downtrends confirms selling pressure Divergence between price trend and SMF warns of potential reversal For Reversal Detection: Bullish divergence at price lows suggests potential bottom Bearish divergence at price highs suggests potential top Strong signals (triangles) with high volume add conviction For Entry Timing: Enter longs when SMF crosses into accumulation zone Enter shorts when SMF crosses into distribution zone Wait for high volume confirmation for stronger signals Use divergences as early warning for position management Alerts Available SMF Accumulation Started — SMF entered accumulation zone SMF Distribution Started — SMF entered distribution zone SMF Strong Accumulation — Accumulation with high volume SMF Strong Distribution — Distribution with high volume SMF Bullish Divergence — Bullish divergence detected SMF Bearish Divergence — Bearish divergence detected Best Practices High volume during accumulation/distribution adds significant conviction Divergences are early warnings—don't trade them alone Use in conjunction with price action and support/resistance Works best on liquid markets with reliable volume data This indicator is provided for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management before making trading decisions. — Made with passion by officialjackofalltrades
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Account GuardianAccount Guardian: Dynamic Risk/Reward Overlay Introduction Account Guardian is an open-source indicator for TradingView designed to help traders evaluate trade setups before entering positions. It automatically calculates Risk-to-Reward ratios based on market structure, displays visual Stop Loss and Take Profit zones, and provides real-time position sizing recommendations. The indicator addresses a fundamental question every trader should ask before entering a trade: "Does this setup make mathematical sense?" Account Guardian answers this question visually and numerically, helping traders avoid impulsive entries with poor risk profiles. Core Functionality Account Guardian performs four primary functions: Detects swing highs and swing lows to identify logical stop loss placement levels Calculates Risk-to-Reward ratios for both long and short setups in real-time Displays visual SL/TP zones on the chart for immediate trade planning Computes position sizing based on your account size and risk tolerance The goal is to provide traders with instant feedback on whether a potential trade meets their minimum risk/reward criteria before committing capital. How It Works Swing Detection The indicator uses pivot point detection to identify recent swing highs and swing lows on the chart. These swing points serve as logical areas for stop loss placement: For Long Trades: The most recent swing low becomes the stop loss level. Price breaking below this level would invalidate the bullish thesis. For Short Trades: The most recent swing high becomes the stop loss level. Price breaking above this level would invalidate the bearish thesis. The swing detection lookback period is configurable, allowing you to adjust sensitivity based on your trading timeframe and style. It automatically adjusts the tp and sl when it is applied to your chart so it is always moving up and down! Risk/Reward Calculation Once swing levels are identified, the indicator calculates: Entry Price: Current close price (where you would enter) Stop Loss: Recent swing low (for longs) or swing high (for shorts) Risk: Distance from entry to stop loss Take Profit: Entry plus (Risk × Target Multiplier) R:R Ratio: Reward divided by Risk The R:R ratio is then evaluated against your configured thresholds to determine if the setup is valid, marginal, or poor. Visual Elements SL/TP Zones When enabled, the indicator draws colored boxes on the chart showing: Red Zone: Stop Loss area - the region between your entry and stop loss Green/Gold/Red Zone: Take Profit area - colored based on R:R quality The color coding provides instant visual feedback: Green: R:R meets or exceeds your "Good R:R" threshold (default 3:1) Gold: R:R meets minimum threshold but below "Good" (between 2:1 and 3:1) Red: R:R below minimum threshold - setup should be avoided Swing Point Markers Small circles mark detected swing points on the chart: Green circles: Swing lows (potential support / long SL levels) Red circles: Swing highs (potential resistance / short SL levels) Dashboard Panel The dashboard in the top-right corner displays comprehensive trade planning information: R:R Row: Current Risk-to-Reward ratio for long and short setups Status Row: VALID, OK, BAD, or N/A based on R:R thresholds Stop Loss Row: Exact price level for stop loss placement Take Profit Row: Exact price level for take profit placement Pos Size Row: Recommended position size based on your risk parameters Risk $ Row: Dollar amount at risk per trade Position Sizing Logic The indicator calculates position size using the formula: Position Size = Risk Amount / Risk per Unit Where: Risk Amount = Account Size × (Risk Percentage / 100) Risk per Unit = Entry Price - Stop Loss Price For example, with a $10,000 account risking 1% per trade ($100), if your entry is at 100 and stop loss at 98 (risk of 2 per unit), your position size would be 50 units. Input Parameters Swing Detection: Swing Lookback: Number of bars to look back for pivot detection (default: 10). Higher values find more significant swing points but may be slower to update. Target Multiplier: Multiplier applied to risk to calculate take profit distance (default: 2). A value of 2 means TP is 2× the distance of SL from entry. Risk/Reward Thresholds: Minimum R:R: Minimum acceptable Risk-to-Reward ratio (default: 2.0). Setups below this show as "BAD" in red. Good R:R: Threshold for excellent setups (default: 3.0). Setups at or above this show as "VALID" in green. Account Settings: Account Size ($): Your trading account size in dollars (default: 10,000). Used for position sizing calculations. Risk Per Trade (%): Percentage of account to risk per trade (default: 1.0%). Professional traders typically risk 0.5-2% per trade. Display: Show SL/TP Zones: Toggle visibility of the colored zone boxes on chart (default: enabled) Show Dashboard: Toggle visibility of the information panel (default: enabled) Analyze Direction: Choose to analyze Long only, Short only, or Both directions (default: Both) How to Use This Indicator Basic Workflow: Add the indicator to your chart Configure your account size and risk percentage in the settings Set your minimum and good R:R thresholds based on your trading rules Look at the dashboard to see current R:R for potential long and short entries Only consider trades where the status shows "VALID" or at minimum "OK" Use the displayed SL and TP levels for your order placement Use the position size recommendation to determine lot/contract size Interpreting the Dashboard: VALID (Green): Excellent setup - R:R meets your "Good" threshold. This is the ideal scenario for taking a trade. OK (Gold): Acceptable setup - R:R meets minimum but isn't optimal. Consider taking if other confluence factors align. BAD (Red): Poor setup - R:R below minimum threshold. Avoid this trade or wait for better entry. N/A (Gray): Cannot calculate - usually means no valid swing point detected yet. Best Practices: Use this indicator as a filter, not a signal generator. It tells you IF a trade makes sense, not WHEN to enter. Combine with your existing entry strategy - use Account Guardian to validate setups from other analysis. Adjust the swing lookback based on your timeframe. Lower timeframes may need smaller lookback values. Be honest with your account size input - accurate position sizing requires accurate inputs. Consider the target multiplier carefully. Higher multipliers mean larger potential reward but lower probability of hitting TP. Alerts The indicator includes four alert conditions: Good Long Setup: Triggers when long R:R reaches or exceeds your "Good R:R" threshold Good Short Setup: Triggers when short R:R reaches or exceeds your "Good R:R" threshold Bad Long Setup: Triggers when long R:R falls below your minimum threshold Bad Short Setup: Triggers when short R:R falls below your minimum threshold These alerts can help you monitor multiple charts and get notified when favorable setups appear. Technical Implementation The indicator is built using Pine Script v6 and includes: Pivot-based swing detection using ta.pivothigh() and ta.pivotlow() Dynamic box drawing for visual SL/TP zones Table-based dashboard for clean information display Color-coded visual feedback system Persistent variable tracking for swing levels Code Structure: // Swing Detection float swingHi = ta.pivothigh(high, swingLen, swingLen) float swingLo = ta.pivotlow(low, swingLen, swingLen) // R:R Calculation for Long float longSL = recentSwingLo float longRisk = entry - longSL float longTP = entry + (longRisk * targetMult) float longRR = (longTP - entry) / longRisk // Position Sizing float riskAmount = accountSize * (riskPct / 100) float posSize = riskAmount / longRisk Limitations The indicator uses historical swing points which may not always represent optimal SL placement for your specific strategy Position sizing assumes you can trade fractional units - adjust accordingly for instruments with minimum lot sizes R:R calculations assume linear price movement and don't account for gaps or slippage The indicator doesn't predict price direction - it only evaluates the mathematical viability of a setup Swing detection has inherent lag due to the lookback period required for pivot confirmation Recommended Settings by Trading Style Scalping (1-5 minute charts): Swing Lookback: 5-8 Target Multiplier: 1-2 Minimum R:R: 1.5 Good R:R: 2.0 Day Trading (15-60 minute charts): Swing Lookback: 8-12 Target Multiplier: 2 Minimum R:R: 2.0 Good R:R: 3.0 Swing Trading (4H-Daily charts): Swing Lookback: 10-20 Target Multiplier: 2-3 Minimum R:R: 2.5 Good R:R: 4.0 Why Risk/Reward Matters Many traders focus solely on win rate, but profitability depends on the combination of win rate AND risk/reward ratio. Consider these scenarios: 50% win rate with 1:1 R:R = Breakeven (before costs) 50% win rate with 2:1 R:R = Profitable 40% win rate with 3:1 R:R = Profitable 60% win rate with 1:2 R:R = Losing money Account Guardian helps ensure you only take trades where the math works in your favor, even if you're wrong more often than you're right. Disclaimer This indicator is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. The calculations provided by this indicator are based on historical price data and mathematical formulas that may not accurately predict future price movements. Position sizing recommendations are estimates based on user inputs and should be verified before placing actual trades. Always consider factors such as leverage, margin requirements, and broker-specific rules when determining actual position sizes. The Risk-to-Reward ratios displayed are theoretical calculations based on swing point detection. Actual trade outcomes will vary based on market conditions, execution quality, and other factors not captured by this indicator. Past performance does not guarantee future results. Users should thoroughly test any trading approach in a demo environment before risking real capital. The authors and publishers of this indicator are not responsible for any losses or damages arising from its use. Always consult with a qualified financial advisor before making investment decisions.
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XAU Seasonality + Setup Quality + Month Strength | WarRoomXYZXAU Seasonality Engine is a technical analysis indicator developed for the study of recurring, calendar-based behavior on XAUUSD (Gold). The tool blends month-of-year seasonality statistics with higher-timeframe context and a setup-quality gate to help users observe when market conditions historically lean strong, weak, or neutral — and how strict trade selection should be during each regime. Indicator Concept An indicator for XAUUSD that combines: 1. Seasonality Regime (Month-of-Year Bias) ► Classifies the current month as Strong / Weak / Neutral based on either: • Preset months (user-defined) or • Auto mode (computed from historical monthly performance) ► Strong months suggest a bullish tailwind (not a signal). ► Weak months suggest headwind / caution and require stricter setup quality. 2. Monthly Performance Engine (Under the Hood) ► Uses the symbol’s monthly timeframe data to compute, per calendar month: • Average monthly return (%) • Win rate (%) — how often that month closes positive • Month Strength Score (0–100) — a blended score derived from performance data ► The score is designed to provide a relative strength snapshot of seasonality by month. 3. Month Strength Histogram ► Plots a histogram (0–100) of the current month’s strength score. • Higher bars = historically stronger month tendency • Lower bars = historically weaker month tendency ► Optional horizontal reference lines mark “strong” and “weak” zones to make regimes obvious at a glance. 4. Setup Quality Meter (Confluence Filter) ► The indicator calculates a Setup Quality Score (0–100) using market structure and momentum components, such as: • EMA trend alignment • Momentum confirmation (EMA fast vs slow) • Structure break confirmation (BOS) • Liquidity sweep behavior • Candle confirmation logic ► This score is intended as a trade-selectivity filter , not a trade executor. 5. Adaptive Rules for Weak Months (Strict Mode) ► When the indicator detects a weak seasonal regime, conditions automatically tighten: • The A+ threshold increases (adaptive thresholding) • Optional rule: Weak months require BOS + Sweep + FVG simultaneously before any A+ condition is considered valid This forces the user into “higher-quality-only” behavior during historically weaker seasonal periods. 🔹1 Visual Components Included • Seasonality regime label (Strong / Weak / Neutral) • Optional background shading based on regime • Month Strength Score histogram (0–100) • Current month stats: Avg return + win rate • Setup Quality Meter value (0–100) • Adaptive A+ threshold display • Weak-month confluence gate status (BOS / Sweep / FVG pass/fail) • Optional alerts when strict criteria are met ➣What Means in the XAU Indicator 🔹 Definition (in THIS indicator) Win Rate = the percentage of historical months that closed positive for the same calendar month. It is NOT: trade win rate ❌ signal accuracy ❌ It is a s tatistical seasonality metric . How It’s Calculated For each calendar month (January, February, etc.), the indicator: 1.Looks at historical monthly candles (Monthly timeframe). 2. Counts how many times that month: •Closed higher than it opened (or higher than previous month close). 3. Divides: Number of positive months ÷ Total number of observed months × 100 Example: September If over the last 20 years: September closed green 14 times September closed red 6 times Then: Win Rate = (14 / 20) × 100 = 70% That’s what you see as in the dashboard. What the Win Rate Is Used For 1️⃣ Part of the Month Strength Score The indicator blends: •Average Monthly Return (%) → measures magnitude •Win Rate (%) → measures consistency Combined into: Month Strength Score (0–100) This avoids a common trap: •A month with 1 huge rally but many losses ≠ reliable •A month with steady positive closes = higher quality environment What Win Rate Tells You High Win Rate (e.g. 65–75%) •Gold more often closes higher in this month •Continuation is statistically more likely •Pullbacks are more likely to resolve in trend direction Low Win Rate (e.g. 35–45%) •Gold more often fails to close higher •More chop, deeper retracements, false breakouts •Continuation trades statistically struggle What It Does NOT Tell You 🚫 It does NOT mean: •“You will win 70% of your trades” •“Every setup in this month works” •“Direction is guaranteed” Seasonality is context, not prediction. Why This Is Powerful When Combined With Your System On its own, win rate is just data. But in your indicator, it’s used to: •🔒 Raise the A+ threshold in weak months •🧠 Force BOS + Sweep + FVG confluence •❌ Block marginal setups automatically So instead of guessing: -“Why is gold so choppy this month?” You know: -“This month historically underperforms SO I must be stricter.” ➣What Means in the XAU Seasonality Indicator 🔹 Definition (in THIS indicator) Avg Monthly Return = the average percentage gain or loss of XAUUSD for a specific calendar month, calculated across many years. It measures magnitude , not frequency. It is NOT: •trade profit ❌ •expected return for the next month ❌ •guaranteed performance ❌ It is a historical seasonality tendency. How It’s Calculated For each calendar month (January, February, etc.), the indicator: 1.Takes every historical occurrence of that month. 2.Calculates the percentage change of the monthly candle: (Monthly Close − Previous Monthly Close) ÷ Previous Monthly Close × 100 3. Adds all those percentage changes together. 4. Divides by the total number of observations. Example: September Assume over 20 years: +2.4%, +1.1%, −0.6%, +3.0%, +1.8%, ... If the sum of all September returns = +28% across 20 years: Avg Monthly Return = +1.40% That’s the number displayed in the indicator. What Avg Monthly Return Is Used For 1️⃣ Measuring Strength of Movement •Win Rate → “How often does it close green?” •Avg Monthly Return → “How big are the moves when it works?” Both are needed. A month can: •Win often but move very little •Move a lot but only occasionally The indicator combines both to avoid misleading conclusions. How to Interpret Avg Monthly Return Positive Avg Return (e.g. +0.8% to +2.0%) •Gold tends to expand during this month •Continuation phases are more likely •Pullbacks are often absorbed Near-Zero Avg Return (e.g. −0.2% to +0.2%) •Market is statistically balanced •Expect chop, rotations, false breaks •Continuation is less reliable Negative Avg Return (e.g. −0.5% or worse) •Downward pressure or heavy mean reversion •Rallies often fade •Risk of aggressive stop hunts What Avg Monthly Return Does NOT Mean 🚫 It does NOT mean: •“Price will move +1.4% this month” •“You should buy because the number is positive” •“This is a guaranteed edge” It describes historical behavior, not future certainty. Why Avg Monthly Return Matters More Than People Think Two months can have the same win rate but behave very differently: Example: Month Win Rate Avg Return Reality Month A 65% +0.2% Small, choppy wins Month B 55% +1.6% Fewer wins, but strong expansions Your indicator would rank Month B as stronger, which is correct for continuation-based strategies. How It Feeds the Month Strength Score The indicator blends: •60% Avg Monthly Return (normalized) •40% Win Rate This means: •Big moves matter more than small consistency •But consistency still matters enough to prevent distortion Result: Month Strength Score (0–100) Which is then used to: •tighten or relax A+ thresholds •activate weak-month strict rules •control trade frequency 🔹2. Intended Use The indicator is designed as a discretionary analysis tool to support study of: • seasonal bias and calendar tendencies • relative strength/weakness across months • how strict trade selection should be across different regimes • confluence behavior when seasonal conditions are unfavorable The tool does not generate forecasts, does not guarantee outcomes, and should not be relied upon as a stand-alone decision mechanism. 🔹3.How to Use XAU Seasonality Engine Recommended charts: XAUUSD, intraday (5m–15m) with a HTF context (1H–4H). 1. Identify the Seasonal Regime • Strong month → you can allow more continuation bias (still require structure). • Neutral month → trade normally, standard criteria. • Weak month → tighten selection, demand clean A+ conditions only. 2. Read the Month Strength Histogram • If the score is high (e.g., 70+), the month has historically shown stronger tendency. • If the score is low (e.g., 40 and below), expect slower conditions, deeper pullbacks, or more chop — and reduce marginal trades. 3. Use the Setup Quality Meter as the Gate ► In normal/strong months: • A+ threshold is moderate (e.g., 70) ► In weak months: • A+ threshold is higher (e.g., 80+) • Optional strict mode: must also pass BOS + Sweep + FVG alignment 4. Example Trade Logic (Framework, Not Signals) ► Bullish framework in a Strong Month: • Seasonal regime = Strong (tailwind) • Structure supports bullish continuation (trend alignment) • Sweep occurs into demand / liquidity grab • Setup Quality reaches A+ threshold • Entry: confirmation candle or retrace to key level • SL: beyond sweep low / invalidation • TP: nearest liquidity / prior highs / HTF level ► Weak Month rule-set (Strict Mode): • Seasonal regime = Weak (headwind) • Only consider trades if: ✅ BOS confirms direction ✅ Sweep occurs and rejects cleanly ✅ FVG exists recently (or is mitigated if you choose that model) ✅ Setup Quality exceeds the elevated adaptive threshold If any one is missing → no trade This is not meant to “predict” gold — it’s meant to enforce discipline when seasonality historically underperforms. 🔹4.Limitations and User Responsibility ► The indicator does not represent financial advice or imply performance expectations. ► Seasonality is statistical tendency, not certainty — macro conditions can override it. ► Results vary by broker feed, timeframe, and settings. ► Users should test thoroughly in simulation before applying to live markets. ► All trading decisions, risk management, and execution remain solely the responsibility of the user. 🔹5. Alerts Optional alerts can notify when: • a new month begins and the seasonal regime changes • A+ criteria are met • weak-month strict conditions pass (BOS + Sweep + FVG) Alerts are informational only and do not constitute actionable recommendations. Disclaimer This script is provided for informational and educational purposes only . It does not provide financial, investment, or trading advice, and it does not guarantee profits or future performance. All decisions made based on this script are solely the responsibility of the user. This script does not execute trades, manage risk, or replace the need for trader discretion. Market behavior can change quickly, and past behavior detected by the script does not ensure similar future outcomes. Users should test the script on demo or simulation environments before applying it to live markets and must maintain full responsibility for their own risk management, position sizing, and trade execution. Trading involves risk, and losses can exceed deposits. By using this script, you acknowledge that you understand and accept all associated risks.
Pine Script® göstergesi
WarroomXYZ tarafından
Multi-Fractal Trading Plan [Gemini] v22Multi-Fractal Trading Plan The Multi-Fractal Trading Plan is a quantitative market structure engine designed to filter noise and generate actionable daily strategies. Unlike standard auto-trendline indicators that clutter charts with irrelevant data, this system utilizes Fractal Geometry to categorize market liquidity into three institutional layers: Minor (Intraday), Medium (Swing), and Major (Institutional). This tool functions as a Strategic Advisor, not just a drawing tool. It calculates the delta between price and structural pivots in real-time, alerting you when price enters high-probability "Hot Zones" and generating a live trading plan on your dashboard. Core Features 1. Three-Tier Fractal Engine The algorithm tracks 15 distinct fractal lengths simultaneously, aggregating them into a clean hierarchy: Minor Structure (Thin Lines): Captures high-frequency volatility for scalping. Medium Structure (Medium Lines): Identifies significant swing points and intermediate targets. Major Structure (Thick Lines): Maps the "Institutional" defense lines where trend reversals and major breakouts occur. 2. The Strategic Dashboard A dynamic data panel in the bottom-right eliminates analysis paralysis: Floor & Ceiling Targets: Displays the precise price levels of the nearest Support and Resistance. AI Logic Output: The script analyzes market conditions to generate a specific command, such as "WATCH FOR BREAKOUT", "Near Lows (Look Long?)", or "WAIT (No Setup)". 3. "Hot Zone" Detection Never miss a critical test of structure. Dynamic Alerting: When price trades within 1% (adjustable) of a Major Trend Line, the indicator’s labels turn Bright Yellow and flash a warning (e.g., "⚠️ WATCH: MAJOR RES"). Focus: This visual cue highlights the exact moment execution is required, reducing screen fatigue. 4. The Quant Web & Markers Pivot Validation: Deep blue fractal markers (▲/▼) identify the exact candles responsible for the structure. Inter-Timeframe Web: Faint dotted lines connect Minor pivots directly to Major pivots, visualizing the "hidden" elasticity between short-term noise and long-term trend anchors. 5. Enterprise Stability Engine Engineered to solve the "Vertical Line" and "1970 Epoch" glitches common in Pine Script trend indicators. This engine is optimized for Futures (NQ/ES), Forex, and Crypto, ensuring stability across all timeframes (including gaps on ETH/RTH charts). Operational Guide Consult the Dashboard: Before executing, check the "Strategy" output. If it says "WAIT", the market is in chop. If it says "WATCH FOR BOUNCE", prepare your entry criteria. Monitor Hot Zones: A Yellow Label indicates price is testing a major liquidity level. This is your signal to watch for a rejection wick or a high-volume breakout. Utilize the Web: Use the faint web lines to find "confluence" where a short-term pullback aligns with a long-term trend line. Configuration Show History: Toggles "Ghost Lines" (Blue) to display historical structure and broken trends. Fractal Points: Toggles the geometric pivot markers. Hot Zone %: Adjusts the sensitivity of the Yellow Warning system (Default: 1%). Max Line Length: A noise filter that removes stale or "spiderweb" lines that are no longer statistically relevant.
Pine Script® göstergesi
CustomQuantLabs tarafından
2
Iridescent Liquidity Prism [JOAT]Iridescent Liquidity Prism | Peer Momentum HUD A multi-layered order-flow indicator that combines microstructure analysis, smart-money footprint detection, and intermarket momentum signals. The script uses dynamic color-shifting themes to visualize liquidity patterns, structure, and peer momentum data directly on the chart. There is so much to choose from inside the settings, if you think it's a mess on the chart it's because you have to personally customize it based on your needs... Core Functionality The indicator calculates and displays several analytical layers simultaneously: Order-Flow Imbalance (OFI): Calculates buy vs. sell volume pressure using volume-weighted price distribution within each bar. Uses an EMA filter (default: 55 periods) to smooth the signal. Values are normalized using standard deviation to identify significant imbalances. Smart Money Footprints: Detects accumulation and distribution zones by comparing volume rate of change (ROC) against price ROC. When volume ROC exceeds a threshold (default: 65%) and price ROC is positive, accumulation is detected. When volume ROC is high but price ROC is negative, distribution is detected. Fractal Structure Mapping: Identifies pivot highs and lows using a fractal detection algorithm (default: 5-bar period). Maintains a rolling window of recent structure points (default: 4 levels) and draws connecting lines to show trend structure. Fair Value Gap (FVG) Detection: Automatically detects price gaps where three consecutive candles create an imbalance. Bullish FVGs occur when the current low exceeds the high two bars ago. Bearish FVGs occur when the current high is below the low two bars ago. Gaps persist for a configurable duration (default: 320 bars) and fade when price fills the gap. Liquidity Void Detection: Identifies candles where the high-low range exceeds an ATR threshold (default: 1.7x ATR) while volume is below average (default: 65% of 20-bar average). These conditions suggest areas where liquidity may be thin. Price/Volume Divergence: Uses linear regression to detect when price trend direction disagrees with volume trend direction. A divergence alert appears when price is trending up while volume is trending down, or vice versa. Peer Momentum Heatmap (PMH): Calculates composite momentum scores for up to 6 symbols across 4 timeframes. Each score combines RSI (default: 14 periods) and StochRSI (default: 14 periods, 3-bar smooth) to create a momentum composite between -1 and +1. The highest absolute momentum score across all combinations is displayed in the HUD. Custom settings using Fractal Pivots, Skeleton Structure, Pulse Liquidity Voids, Bottom Colorful HeatMaps, and Iridescent Field. --- Visual Components Spectrum Aura Glow: ATR-weighted bands (default: 0.25x ATR) that expand and contract around price action, indicating volatility conditions. The thickness adapts to market volatility. Chromatic Flow Trail: A blended line combining EMA and WMA of price (default: 8-period EMA blended with WMA at 65% ratio). The trail uses gradient colors that shift based on a phase oscillator, creating an iridescent effect. Volume Heat Projection: Creates horizontal volume profile bands at price levels (default: 14 levels). Scans recent bars (default: 150 bars) to calculate volume concentration. Each level is colored based on its volume density relative to the maximum volume level. Structure Skeleton: Dashed lines connecting fractal pivot points. Uses two layers: a primary line (2-3px width) and an optional glow overlay (4-5px width) for enhanced visibility. Fractal Markers: Diamond shapes placed at pivot high and low points. Color-coded: primary color for highs, secondary color for lows. Iridescent Color Themes: Five color themes available: Iridescent (default), Pearlescent, Prismatic, ColorShift, and Metallic. Colors shift dynamically using a phase oscillator that cycles through the color spectrum based on bar index and a speed multiplier (default: 0.35). --- HUD Console Metrics The right-side HUD displays seven key metrics: Flow: Shows OFI status: ▲ FLOW BUY when normalized OFI exceeds imbalance threshold (default: 2.2), ▼ FLOW SELL when below -2.2, or ◆ FLOW BAL when balanced. Struct: Structure trend bias: ▲ STRUCT BULL when microtrend > 2, ▼ STRUCT BEAR when < -2, or ◆ STRUCT RANGE when neutral. Smart$: Institutional activity: ◈ ACCUM when smart money index = 1, ◈ DISTRIB when = -1, or ○ IDLE when inactive. Liquid: Liquidity state: ⚡ VOID when a liquidity void is detected, or ● NORMAL otherwise. Diverg: Divergence status: ⚠ ALERT when price/volume divergence detected, or ✓ CLEAR when aligned. PMH: Peer Momentum Heatmap status: Shows dominant timeframe and momentum score. Displays 🪩 for bull surge (above 0.55 threshold) or 🧨 for bear surge (below -0.55). FVG: Fair Value Gap status: Shows active gap count or CLEAR when no gaps exist. Displays GAP LONG when bullish gap detected, GAP SHORT when bearish gap detected. Pearlscent Color with Volume Heatmap. Parameters and Settings Microstructure Engine: Analysis Depth: 20-250 bars (default: 55) - Controls OFI smoothing period Liquidity Threshold ATR: 1.0-4.0 (default: 1.7) - Multiplier for void detection Imbalance Ratio: 1.5-6.0 (default: 2.2) - Standard deviations for OFI significance Smart Money Layer: Smart Money Window: 10-150 bars (default: 24) - Period for ROC calculations Accumulation Threshold: 40-95% (default: 65%) - Volume ROC threshold Structural Mapping: Fractal Pivot Period: 3-15 bars (default: 5) - Period for pivot detection Structure Memory: 2-8 levels (default: 4) - Number of structure points to track Volume Heat Projection: Heat Map Lookback: 60-400 bars (default: 150) - Bars to analyze for volume profile Heat Map Levels: 5-30 levels (default: 14) - Number of price level bands Heat Map Opacity: 40-100% (default: 92%) - Transparency of heat map boxes Heat Map Width Limit: 6-80 bars (default: 26) - Maximum width of heat map boxes Heat Map Visibility Threshold: 0.0-0.5 (default: 0.08) - Minimum density to display Iridescent Enhancements: Visual Theme: Iridescent, Pearlescent, Prismatic, ColorShift, or Metallic Color Shift Speed: 0.05-1.00 (default: 0.35) - Speed of color phase oscillation Aura Thickness (ATR): 0.05-1.0 (default: 0.25) - Multiplier for aura band width Chromatic Trail Length: 2-50 bars (default: 8) - Period for trail calculation Trail Blend Ratio: 0.1-0.95 (default: 0.65) - EMA/WMA blend percentage FVG Persistence: 50-600 bars (default: 320) - Bars to keep FVG boxes active Max Active FVG Boxes: 10-200 (default: 40) - Maximum boxes on chart FVG Base Opacity: 20-95% (default: 80%) - Transparency of FVG boxes Peer Momentum Heatmap: Peer Symbols: Comma-separated list of up to 6 symbols (e.g., "BTCUSD,ETHUSD") Peer Timeframes: Comma-separated list of up to 4 timeframes (default: "60,240,D") PMH RSI Length: 5-50 periods (default: 14) PMH StochRSI Length: 5-50 periods (default: 14) PMH StochRSI Smooth: 1-10 periods (default: 3) Super Momentum Threshold: 0.2-0.95 (default: 0.55) - Threshold for surge detection Clarity & Readability: Liquidity Void Opacity: 5-90% (default: 30%) Smart Money Footprint Opacity: 5-90% (default: 35%) HUD Background Opacity: 40-95% (default: 70%) Iridescent Field: Field Opacity: 20-100% (default: 86%) - Background color intensity Field Smooth Length: 10-200 bars (default: 34) - Smoothing for background gradient --- Alerts The indicator provides seven alert conditions: Liquidity Void Detected - Triggers when void conditions are met Strong Order Flow - Triggers when normalized OFI exceeds imbalance ratio Smart Money Activity - Triggers when accumulation or distribution detected Price/Volume Divergence - Triggers when divergence conditions occur Structure Shift - Triggers when structure polarity changes significantly PMH Bull Surge - Triggers when PMH exceeds positive threshold (if enabled) PMH Bear Surge - Triggers when PMH exceeds negative threshold (if enabled) Bull/Bear Prismatic FVG - Triggers when new FVG is detected (if FVG display enabled) --- Usage Considerations Performance may vary on lower timeframes due to the volume heat map calculations scanning multiple bars. Consider reducing heat map lookback or levels if experiencing slowdowns. The PMH feature requires data requests to other symbols/timeframes, which may impact performance. Limit the number of peer symbols and timeframes for optimal performance. FVG boxes automatically expire after the persistence period to prevent chart clutter. The maximum box limit (default: 40) prevents excessive memory usage. Color themes affect all visual elements. Choose a theme that provides good contrast with your chart background. The indicator is designed for overlay display. All visual elements are positioned relative to price action. Structure lines are drawn dynamically as new pivots form. On fast-moving markets, structure may update frequently. Volume calculations assume typical volume data availability. Symbols without volume may show incomplete data for volume-dependent features. --- Technical Notes Built on Pine Script v6 with dynamic request capability for PMH functionality. Uses exponential moving averages (EMA) and weighted moving averages (WMA) for trail calculations to balance responsiveness and smoothness. Volume profile calculation uses price level buckets. Higher levels provide finer granularity but require more computation. Iridescent color engine uses a phase oscillator with sine wave calculations for smooth color transitions. Box management includes automatic cleanup of expired boxes to maintain performance. All visual elements use color gradients and transparency for smooth blending with price action. --- Customization Examples Intraday Scalping Setup: Analysis Depth: 30 bars Heat Map Lookback: 100 bars FVG Persistence: 150 bars PMH Window: 15 bars Fast color shift speed: 0.5+ Macro Structure Tracking: Analysis Depth: 100+ bars Heat Map Lookback: 300+ bars FVG Persistence: 500+ bars Structure Memory: 6-8 levels Slower color shift speed: 0.2 --- Limitations Volume heat map calculations may be computationally intensive on lower timeframes with high lookback values. PMH requires valid symbol names and accessible timeframes. Invalid symbols or timeframes will return no data. FVG detection requires at least 3 bars of history. Early bars may not show FVG boxes. Structure lines connect points but do not predict future structure. They reflect historical pivot relationships. Color themes are aesthetic choices and do not affect calculation logic. The indicator does not provide trading signals. All visual elements are analytical tools that require interpretation in context of market conditions. Open Source This indicator is open source and available for modification and distribution. The code is published with Pine Script v6 compliance. Users are free to customize parameters, modify calculations, and adapt the visual elements to their trading needs. For questions, suggestions, or anything please talk to me in private messages or comments below! Would love to help! - officialjackofalltrades
Pine Script® göstergesi
officialjackofalltrades tarafından
1
Liquidity Maxing [JOAT]Liquidity Maxing - Institutional Liquidity Matrix Introduction Liquidity Maxing is an open-source strategy for TradingView built around institutional market structure concepts. It identifies structural shifts, evaluates trades through multi-factor confluence, and implements layered risk controls. The strategy is designed for swing trading on 4-hour timeframes, focusing on how institutional order flow manifests in price action through structure breaks, inducements, and liquidity sweeps. Core Functionality Liquidity Maxing performs three primary functions: Tracks market structure to identify when control shifts between buyers and sellers Scores potential trades using an eight-factor confluence system Manages position sizing and risk exposure dynamically based on volatility and user-defined limits The goal is selective trading when multiple conditions align, rather than frequent entries. Market Structure Engine The structure engine tracks three key events: Break of Structure (BOS): Price pushes beyond a prior pivot in the direction of trend Change of Character (CHoCH): Control flips from bullish to bearish or vice versa Inducement Sweeps (IDM): Market briefly runs stops against trend before moving in the real direction The structure module continuously updates strong highs and lows, labeling structural shifts visually. IDM markers are optional and disabled by default to maintain chart clarity. The trade engine requires valid structure alignment before considering entries. No structure, no trade. Eight-Factor Confluence System Instead of relying on a single indicator, Liquidity Maxing uses an eight-factor scoring system: Structure alignment with current trend RSI within healthy bands (different ranges for up and down trends) MACD momentum agreement with direction Volume above adaptive baseline Price relative to main trend EMA Session and weekend filter (configurable) Volatility expansion/contraction via ATR shifts Higher-timeframe EMA confirmation Each factor contributes one point to the confluence score. The default minimum confluence threshold is 6 out of 8, but you can adjust this from 1-8 based on your preference for trade frequency versus selectivity. Only when structure and confluence agree does the strategy proceed to risk evaluation. Dynamic Risk Management Risk controls are implemented in multiple layers: ATR-based stops and targets with configurable risk-to-reward ratio (default 2:1) Volatility-adjusted position sizing to maintain consistent risk per trade as ranges expand or compress Daily and weekly risk budgets that halt new entries once thresholds are reached Correlation cooldown to prevent clustered trades in the same direction Global circuit breaker with maximum drawdown limit and emergency kill switch If any guardrail is breached, the strategy will not open new positions. The dashboard clearly displays risk state for transparency. Market Presets The strategy includes configuration presets optimized for different market types: Crypto (BTC/ETH): RSI bands 70/30, volume multiplier 1.2, enhanced ATR scaling Forex Majors: RSI bands 75/25, volume multiplier 1.5 Indices (SPY/QQQ): RSI bands 70/30, volume multiplier 1.3 Custom: Default values for user customization For crypto assets, the strategy automatically applies ATR volatility scaling to account for higher volatility characteristics. Monitoring and Dashboards The strategy includes optional monitoring layers: Risk Operations Dashboard (top-right): Trend state Confluence score ATR value Current position size percentage Global drawdown Daily and weekly risk consumption Correlation guard state Alert mode status Performance Console (top-left): Net profit Current equity Win rate percentage Average trade value Sharpe-style ratio (rolling 50-bar window) Profit factor Open trade count Optional risk tint on chart background provides visual indication of "safe to trade" versus "halted" state. All visualization elements can be toggled on/off from the inputs for clean chart viewing or full telemetry during parameter tuning. Alerts and Automation The strategy supports alert integration with two formats: Standard alerts: Human-readable messages for long, short, and risk-halt conditions Webhook format: JSON-formatted payloads ready for external execution systems (optional) Alert messages are predictable and unambiguous, suitable for manual review or automated forwarding to execution engines. Built-in Validation Suite The strategy includes an optional validation layer that can be enabled from inputs. It checks: Internal consistency of structure and confluence metrics Sanity and ordering of risk parameters Position sizing compliance with user-defined floors and caps This validation is optional and not required for trading, but provides transparency into system operation during development or troubleshooting. Strategy Parameters Market Presets: Configuration Preset: Choose between Crypto (BTC/ETH), Forex Majors, Indices (SPY/QQQ), or Custom Market Structure Architecture: Pivot Length: Default 5 bars Filter by Inducement (IDM): Default enabled Visualize Structure: Default enabled Structure Lookback: Default 50 bars Risk & Capital Preservation: Risk:Reward Ratio: Default 2.0 ATR Period: Default 14 ATR Multiplier (Stop): Default 2.0 Max Drawdown Circuit Breaker: Default 10% Risk per Trade (% Equity): Default 1.5% Daily Risk Limit: Default 6% Weekly Risk Limit: Default 12% Min Position Size (% Equity): Default 0.25% Max Position Size (% Equity): Default 5% Correlation Cooldown (bars): Default 3 Emergency Kill Switch: Default disabled Signal Confluence: RSI Length: Default 14 Trend EMA: Default 200 HTF Confirmation TF: Default Daily Allow Weekend Trading: Default enabled Minimum Confluence Score (0-8): Default 6 Backtesting Considerations When backtesting this strategy, consider the following: Commission: Default 0.05% (adjustable in strategy settings) Initial Capital: Default $100,000 (adjustable) Position Sizing: Uses percentage of equity (default 2% per trade) Timeframe: Optimized for 4-hour charts, though can be tested on other timeframes Results will vary significantly based on: Market conditions and volatility regimes Parameter settings, especially confluence threshold Risk limit configuration Symbol characteristics (crypto vs forex vs equities) Past performance does not guarantee future results. Win rate, profit factor, and other metrics should be evaluated in context of drawdown periods, trade frequency, and market conditions. How to Use This Strategy This is a framework that requires understanding and parameter tuning, not a one-size-fits-all solution. Recommended workflow: Start on 4-hour timeframe with default parameters and appropriate market preset Run backtests and study performance console metrics: focus on drawdown behavior, win rate, profit factor, and trade frequency Adjust confluence threshold to match your risk appetite—higher thresholds mean fewer but more selective trades Set realistic daily and weekly risk budgets appropriate for your account size and risk tolerance Consider ATR multiplier adjustments based on market volatility characteristics Only connect alerts or automation after thorough testing and parameter validation Treat this as a risk framework with an integrated entry engine, not merely an entry signal generator. The risk controls are as important as the trade signals. Strategy Limitations Designed for swing trading timeframes; may not perform optimally on very short timeframes Requires sufficient market structure to identify pivots; may struggle in choppy or low-volatility environments Crypto markets require different parameter tuning than traditional markets Risk limits may prevent entries during favorable setups if daily/weekly budgets are exhausted Correlation cooldown may delay entries that would otherwise be valid Backtesting results depend on data quality and may not reflect live trading with slippage Design Philosophy Many indicators tell you when price crossed a moving average or RSI left oversold. This strategy addresses questions institutional traders ask: Who is in control of the market right now? Is this move structurally significant or just noise? Do I want to add more risk given what I've already done today/week? If I'm wrong, exactly how painful can this be? The strategy provides disciplined, repeatable answers to these questions through systematic structure analysis, confluence filtering, and multi-layer risk management. Technical Implementation The strategy uses Pine Script v6 with: Custom types for structure, confluence, and risk state management Functional programming approach for reusable calculations State management through persistent variables Optional visual elements that can be toggled independently The code is open-source and can be modified to suit individual needs. All important logic is visible in the source code. Disclaimer This script is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by historical tests of strategies, is not indicative of future results. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between backtested results and actual results subsequently achieved by any particular trading strategy. The user should be aware of the risks involved in trading and should trade only with risk capital. The authors and publishers of this script are not responsible for any losses or damages, including without limitation, any loss of profit, which may arise directly or indirectly from use of or reliance on this script. This strategy uses technical analysis methods and indicators that are not guaranteed to be accurate or profitable. Market conditions change, and strategies that worked in the past may not work in the future. Users should thoroughly test any strategy in a paper trading environment before risking real capital. Commission and slippage settings in backtests may not accurately reflect live trading conditions. Real trading results will vary based on execution quality, market liquidity, and other factors not captured in backtesting. The user assumes full responsibility for all trading decisions made using this script. Always consult with a qualified financial advisor before making investment decisions. Enjoy - officialjackofalltrades
Pine Script® stratejisi
officialjackofalltrades tarafından
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context. 🟢 How It Works The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion: basis = ma(close, length, maType) stdev = ta.stdev(close, length) The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons: zScore = stdev != 0 ? (close - basis) / stdev : 0 smoothedZ = ma(zScore, smooth, maType) The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes: upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes. 🟢 How to Use This Indicator ▶ Overbought and Oversold Identification: The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions. ▶ Mean Reversion Trading Applications: The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway. ▶ Trend and Distribution Analysis: Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears. ▶ Built-in Alerts: Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities. ▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
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