Probabilistic Bias Engine [JOAT]Probabilistic Bias Engine
Introduction
The Probabilistic Bias Engine (PBE) is an advanced open-source directional bias indicator that combines Bayesian probability analysis, historical for-loop pattern recognition, multi-timeframe confluence detection, and ensemble learning to quantify market directional bias with statistical confidence. This indicator transforms raw price action into probabilistic bias scores (0-100%), helping traders identify high-probability directional setups through systematic analysis of historical price behavior across multiple timeframes.
Unlike simple trend indicators that use moving averages or momentum oscillators, PBE employs a sophisticated for-loop analysis system that compares current price against historical price points across customizable lookback periods, applies Bayesian probability theory to calculate directional likelihood, and aggregates signals across multiple timeframes to generate confidence-weighted bias scores. The indicator provides both current timeframe bias and multi-timeframe confluence analysis for comprehensive directional assessment.
Why This Indicator Exists
This indicator addresses the challenge of quantifying directional bias with statistical rigor. Traditional trend indicators provide binary signals (bullish/bearish) without probability quantification. PBE systematically analyzes historical price behavior to reveal:
Bayesian Probability Calculation: Converts for-loop analysis into probabilistic bias scores using Bayesian inference
Historical Pattern Recognition: Analyzes price position relative to 1-70 historical bars to identify directional patterns
Multi-Timeframe Confluence: Confirms bias across short (5m), medium (15m), and long (60m) timeframes
Ensemble For-Loop Analysis: Combines multiple lookback periods (30, 70, 150 bars) for robust bias calculation
Volatility Regime Scaling: Adjusts probability scores based on current volatility environment
Divergence Confirmation Layer: Detects RSI divergences to enhance signal quality
Confidence Heatmap: Visualizes setup quality through multi-factor confidence scoring (0-100%)
Each component provides unique intelligence. For-loop analysis shows historical price position, Bayesian calculation quantifies probability, MTF confluence shows conviction, ensemble analysis adds robustness, volatility scaling adjusts for regime, divergence layer confirms reversals, and confidence scoring synthesizes all factors.
Core Components Explained
1. For-Loop Historical Analysis
PBE's core innovation is systematic comparison of current price against historical price points:
f_forloop_analysis(float src, int start, int lookback) =>
float sum = 0.0
for i = start to lookback
sum += src > src ? 1 : -1
float normalized = sum / (lookback - start + 1)
normalized
This function iterates through historical bars, adding +1 when current price is above historical price and -1 when below. The normalized result ranges from -1.0 (price below all historical points) to +1.0 (price above all historical points).
2. Bayesian Probability Calculation
The for-loop score is converted to probability using Bayesian inference:
f_bayesian_probability(float loop_value) =>
float evidence = loop_value > 0 ? 0.7 : 0.3
float prior = 0.5
float posterior = (prior * evidence) /
(prior * evidence + (1 - prior) * (1 - evidence))
posterior
This calculates the posterior probability of bullish bias given the for-loop evidence. Positive loop values increase bullish probability, negative values increase bearish probability. The result is scaled to 0-100% for display.
image]https://www.pulsewire.com/x/CtYqgABU/
3. Multi-Timeframe Confluence Detection
PBE requests bias data from three timeframes and counts alignment:
f_get_timeframe_bias(string tf) =>
= request.security(syminfo.tickerid, tf,
)
float prob_tf = f_bayesian_probability(loop_score_tf)
int bias_tf = prob_tf > 0.5 ? 1 : -1
Confluence is calculated by counting how many timeframes agree:
Strong Aligned (4/4): All timeframes bullish or bearish - highest conviction
Aligned (3/4): Majority alignment - moderate conviction
Weak (2/4): Split alignment - low conviction
No Alignment (1/4 or 0/4): Conflicting signals - no conviction
4. Ensemble For-Loop Analysis
Multiple lookback periods are combined for robust bias calculation:
f_forloop_ensemble(float src, int start, int end1, int end2, int end3) =>
// Calculate for-loop scores for 30, 70, and 150 bar lookbacks
float norm1 = sum1 / (end1 - start + 1)
float norm2 = sum2 / (end2 - start + 1)
float norm3 = sum3 / (end3 - start + 1)
// Weighted ensemble (shorter periods get more weight)
float ensemble = (norm1 * 0.5) + (norm2 * 0.3) + (norm3 * 0.2)
ensemble
Short-term bias (30 bars) receives 50% weight, medium-term (70 bars) receives 30%, and long-term (150 bars) receives 20%. This creates a balanced view across multiple time horizons.
5. Volatility Regime Scaling
Probability scores are adjusted based on volatility environment:
float atr_val = ta.atr(14)
float natr = (atr_val / close) * 100
float vol_percentile = ta.percentrank(natr, 100)
float regime_multiplier =
vol_percentile >= 80 ? 0.85 : // High vol: reduce confidence
vol_percentile >= 60 ? 0.92 : // Elevated: slight reduction
vol_percentile >= 40 ? 1.0 : // Normal: no adjustment
vol_percentile >= 20 ? 1.05 : // Low vol: slight increase
1.1 // Very low: increase confidence
float regime_adjusted_prob = smoothed_probability * regime_multiplier
High volatility reduces probability scores (more uncertainty), while low volatility increases scores (more predictable).
6. Divergence Confirmation Layer
RSI divergences are detected to enhance signal quality:
float rsi = ta.rsi(close, 14)
// Bullish divergence: price lower low, RSI higher low
bool bull_divergence = low < last_rsi_low_price and rsi > last_rsi_low
// Bearish divergence: price higher high, RSI lower high
bool bear_divergence = high > last_rsi_high_price and rsi < last_rsi_high
Divergences add 20 points to confidence score and trigger enhanced signals when combined with probability alignment.
7. Confidence Heatmap Visualization
Multi-factor confidence scoring (0-100%) based on:
Probability Strength (0-40 points): Distance from 50% neutral (max 40 points at 100% or 0%)
MTF Alignment (0-30 points): 30 points for 4/4 alignment, 20 for 3/4, 10 for 2/4
Divergence Confirmation (0-20 points): 20 points when divergence detected
Regime Favorability (0-10 points): 10 points for Normal/Low vol, 5 for Very Low, 0 for High vol
Total confidence score determines background heatmap intensity:
80-100%: Strong signal (bright color, low transparency)
60-79%: Moderate signal (medium color, medium transparency)
40-59%: Weak signal (dim color, high transparency)
0-39%: No signal (neutral color)
Visual Elements
Probability Line: Main plot showing smoothed probability (0-100%) with dynamic coloring
Zero-Lag Line: Circles overlay showing zero-lag probability for early signals
Histogram: Gradient-colored histogram showing probability deviation from 50% neutral
Reference Lines: 70% (strong bullish), 50% (neutral), 30% (strong bearish)
Background Zones: Strong bullish (>70%), strong bearish (<30%) with transparency
Confidence Heatmap: Background intensity based on multi-factor confidence score
Signal Shapes: High conviction bull/bear setups, regime shifts, divergence confirmations
Dashboard: Real-time metrics including current probability, strength, MTF alignment, ensemble score, volatility regime, confidence, and divergence status
Input Parameters
Bayesian Parameters:
Price Source: Data source for calculations (default: hlc3)
Bayesian Period: Smoothing period for probability (default: 14)
Signal Smoothing: EMA smoothing for final probability (default: 2)
Historical Analysis:
Loop Start: Starting bar for for-loop analysis (default: 1)
Loop Lookback: Ending bar for for-loop analysis (default: 70)
Multi-Timeframe Confluence:
Enable MTF Confluence: Toggle multi-timeframe analysis (default: enabled)
Short Timeframe: Fast timeframe for confluence (default: 5m)
Medium Timeframe: Medium timeframe for confluence (default: 15m)
Long Timeframe: Slow timeframe for confluence (default: 60m)
Confluence Requirement: Minimum timeframes required (default: 2)
Visualization:
Show Probability Bands: Toggle 70%/30% reference lines
Show Bias Zones: Toggle background coloring for strong bias
Show Histogram: Toggle probability deviation histogram
How to Use This Indicator
Step 1: Monitor Probability Level
Watch the main probability line. >70% indicates strong bullish bias, <30% indicates strong bearish bias, 40-60% is neutral.
Step 2: Check MTF Confluence
Verify dashboard shows "Strong Aligned" or "Aligned" status. Higher alignment = higher conviction.
Step 3: Assess Confidence Score
Dashboard confidence >70% indicates high-quality setup. >80% is exceptional.
Step 4: Confirm with Ensemble
Ensemble probability should align with current probability. Divergence suggests conflicting time horizons.
Step 5: Consider Volatility Regime
"Normal" or "Low Vol" regimes have higher reliability. "High Vol" regimes require extra caution.
Step 6: Wait for High Conviction Signals
Best setups occur when:
- Probability >65% or <35%
- Confidence >70%
- MTF alignment 3/4 or 4/4
- Cooldown period passed (12+ bars since last signal)
Best Practices
Use probability crossovers of 50% as regime shift signals
Combine with price action - probability shows bias, price shows execution
MTF alignment is most reliable during trending markets
Confidence heatmap provides quick visual assessment of setup quality
Divergence signals add significant edge when combined with probability alignment
Ensemble probability provides longer-term context - use for position bias
Volatility regime scaling is critical - reduce size in high vol environments
Zero-lag line provides early warning of probability shifts
Histogram intensity shows conviction - larger bars = stronger bias
Indicator Limitations
For-loop analysis is computationally intensive - may slow on lower-end devices
Probability scores are based on historical patterns - unprecedented events can invalidate
MTF confluence requires sufficient data on all timeframes
Bayesian calculation assumes price behavior follows historical patterns
High volatility reduces probability reliability - regime scaling helps but doesn't eliminate
Divergence detection requires clear pivot formation - may lag in choppy markets
Confidence scoring is multi-factor but still probabilistic - not deterministic
Zero-lag calculation can produce whipsaws during consolidation
Technical Implementation
Built with Pine Script v6 using:
Custom for-loop historical analysis across 1-70 bars
Bayesian probability calculation with evidence-based inference
Multi-timeframe security requests for 5m, 15m, 60m confluence
Ensemble for-loop analysis with weighted averaging (30, 70, 150 bars)
ATR-based volatility regime classification with percentile ranking
RSI divergence detection using pivot analysis
Multi-factor confidence scoring (probability, MTF, divergence, regime)
Zero-lag EMA calculation for early signal detection
Gradient histogram with dynamic coloring based on probability
Confidence heatmap background with intensity scaling
Signal cooldown system (12 bars minimum) to prevent overtrading
The code is fully open-source and can be modified to suit individual trading styles.
Originality Statement
This indicator is original in its probabilistic bias quantification approach. While for-loop analysis and Bayesian probability are established concepts, this indicator is justified because:
It combines systematic for-loop historical analysis with Bayesian probability theory for statistical rigor
The ensemble for-loop system (30, 70, 150 bars) with weighted averaging is unique
Multi-timeframe confluence detection provides conviction measurement across 4 timeframes
Volatility regime scaling adjusts probability scores based on market environment
Divergence confirmation layer adds reversal detection to directional bias
Multi-factor confidence scoring (probability + MTF + divergence + regime) synthesizes all components
Zero-lag overlay provides early warning system for probability shifts
Confidence heatmap visualization makes setup quality immediately apparent
Each component contributes unique information: for-loop shows historical position, Bayesian quantifies probability, MTF shows conviction, ensemble adds robustness, volatility scales for regime, divergence confirms reversals, confidence synthesizes quality, and zero-lag provides early warning. The indicator's value lies in presenting these complementary perspectives simultaneously with unified probabilistic framework.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Probability scores do not guarantee outcomes. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
-Made with passion by officialjackofalltrades Indicator
Liquidity Absorption Detector [JOAT]Liquidity Absorption Detector
Introduction
The Liquidity Absorption Detector (LAD) is an advanced open-source multi-timeframe volume analysis indicator that identifies institutional liquidity absorption zones through VWAP deviation analysis, volume surge detection, and oscillator sigma gap confirmation. This indicator reveals when smart money is actively absorbing liquidity at extreme price deviations from VWAP across 2-minute, 5-minute, and 15-minute timeframes, providing traders with high-probability reversal zones where institutional players are positioning.
Unlike basic VWAP indicators that simply plot a mean line, LAD quantifies the statistical deviation from VWAP using three calculation methods (Price Volatility, Z-Score, Spread StDev), detects volume surges relative to historical averages, and confirms absorption through oscillator divergence analysis. The indicator aggregates signals across multiple timeframes to identify zones where 2 or more timeframes show simultaneous absorption, indicating institutional-grade conviction.
ETH chart 45m Timeframe with VOL Signals for huge institutional candlesticks
Why This Indicator Exists
This indicator addresses the challenge of identifying institutional liquidity absorption in real-time. When large players enter positions, they create detectable signatures: extreme VWAP deviations combined with volume surges and oscillator divergences. LAD systematically detects these patterns across multiple timeframes to reveal:
Multi-Timeframe VWAP Deviation: Tracks price deviation from VWAP on 2m, 5m, and 15m timeframes using adaptive thresholds
Volume Surge Detection: Identifies when volume exceeds historical average by customizable multiplier (default 2.25x)
Relative Volume Filtering: Ensures absorption occurs during meaningful volume periods (RVOL >= 0.6)
Oscillator Sigma Gap: Confirms absorption through divergence between VWAP deviation and oscillator z-scores
9-Layer Gradient Ribbon: Visualizes absorption intensity through dynamic color-coded ribbon
Institutional Dashboard: Displays real-time metrics including order flow, liquidity pressure, absorption strength, and signal quality
Each component provides unique intelligence. VWAP deviation shows price extremes, volume surge shows institutional activity, RVOL filters noise, oscillator gap confirms divergence, and timeframe alignment shows conviction. Together, they create a comprehensive institutional absorption detection system.
Core Components Explained
1. Multi-Timeframe VWAP Deviation Analysis
LAD calculates VWAP deviation across three timeframes using your selected method:
f_calculate_vwap_deviation(float price, float vwap_val, string method) =>
float result = 0.0
if method == "Price Volatility"
price_stdev = ta.stdev(price, 20)
denominator = price_stdev * 1.5
result := (price - vwap_val) / denominator
// Additional methods: Z-Score, Spread StDev
result
The indicator requests data from 2m, 5m, and 15m timeframes and applies adaptive thresholds based on volatility regime. When deviation exceeds threshold AND volume surge is detected, an absorption signal is generated.
2. Volume Surge & RVOL Filtering
Volume surge detection identifies when current volume exceeds the moving average by your specified multiplier:
f_volume_surge_detected(int lookback, float multiplier) =>
float avg_vol = ta.sma(volume, lookback)
bool surge = volume > avg_vol * multiplier
surge
RVOL (Relative Volume) filtering ensures signals occur during meaningful volume periods, eliminating low-liquidity false signals.
3. Oscillator Sigma Gap Confirmation
LAD calculates the sigma gap between VWAP deviation and oscillator z-scores (Williams %R and CVD):
float gap_willr = math.abs(dev_5m - willr_zscore)
float gap_cvd = math.abs(dev_5m - cvd_zscore)
float osc_sigma_gap = math.max(gap_willr, gap_cvd)
bool gap_confirmed = osc_sigma_gap >= gap_threshold
When oscillators diverge from VWAP deviation by 4.5+ sigma, it confirms institutional absorption is occurring despite price extremes.
4. Signal Aggregation & Confluence
LAD aggregates signals across all three timeframes:
int buy_signals = (signal_2m and dev_2m < 0 ? 1 : 0) +
(signal_5m and dev_5m < 0 ? 1 : 0) +
(signal_15m and dev_15m < 0 ? 1 : 0)
bool absorption_buy_zone = buy_signals >= 2
Absorption zones require 2+ timeframe confirmation, ensuring high-probability setups. Buy zones occur when price is below VWAP with volume surge across multiple timeframes. Sell zones occur when price is above VWAP with volume surge.
5. 9-Layer Gradient Ribbon Visualization
The gradient ribbon visualizes absorption intensity through 9 transparent layers between VWAP and the wave level:
float wave_ratio = math.min(0.65, math.abs(dev_5m) / threshold_5m)
float wave_level = current_vwap + ((close - current_vwap) * wave_ratio)
// 9 layers calculated with progressive transparency
Ribbon color indicates direction (cyan for buy absorption, magenta for sell absorption) and intensity increases with deviation magnitude.
6. Institutional Dashboard Metrics
The dashboard displays four key institutional metrics:
Order Flow: CVD z-score measuring buy/sell imbalance (threshold: 1.5)
Liquidity Pressure: Average deviation across timeframes vs threshold
Absorption Strength: Number of timeframe confirmations (2/3 or 3/3)
Signal Quality: Deviation strength relative to threshold as percentage
Additional metrics include Oscillator Gap confirmation, RVOL status, and zone classification (Buy Zone, Sell Zone, Neutral).
Showing Liquidity Sell Zone about to occur based of confluences:
Visual Elements
VWAP Line: Dynamic color (cyan for buy zones, magenta for sell zones, neutral otherwise)
Threshold Bands: 2m, 5m, and 15m deviation bands showing absorption thresholds
9-Layer Gradient Ribbon: Progressive transparency showing absorption intensity
Background Zones: Cyan for buy absorption zones, magenta for sell absorption zones
Absorption Labels: "LIQUIDITY ABSORPTION" or "LIQUIDITY DISTRIBUTION" with signal details
Extreme Labels: "EXTREME ABSORPTION/DISTRIBUTION" for highest conviction signals
Dashboard: Real-time institutional metrics in top-right corner
Input Parameters
Core Parameters:
VWAP Calculation: Session Anchored or Continuous
Deviation Method: Price Volatility, Z-Score, or Spread StDev
Volume Lookback: Period for volume average (default: 45)
Volume Surge Multiplier: Threshold for surge detection (default: 2.25x)
RVOL Threshold: Minimum relative volume (default: 0.6)
Multi-Timeframe Thresholds:
2m Threshold: Deviation threshold for 2-minute timeframe (default: 8.0)
5m Threshold: Deviation threshold for 5-minute timeframe (default: 8.0)
15m Threshold: Deviation threshold for 15-minute timeframe (default: 4.0)
Visualization:
Show Absorption Zones: Toggle background coloring
Show VWAP Line: Toggle VWAP display
Zone Transparency: Adjust background opacity (default: 85%)
Ribbon Brightness: Adjust gradient ribbon intensity (-30 to +30)
How to Use This Indicator
Step 1: Identify Absorption Zones
Watch for cyan (buy) or magenta (sell) background zones indicating 2+ timeframe confirmation of absorption.
Step 2: Check Dashboard Metrics
Verify Order Flow, Liquidity Pressure, and Absorption Strength align with the zone direction. Signal Quality >100% indicates strong deviation.
Step 3: Confirm with Oscillator Gap
Look for "CONF" status in Osc Gap row, indicating oscillator divergence confirms absorption.
Step 4: Monitor RVOL
Ensure RVOL shows "HIGH" status, confirming absorption occurs during meaningful volume.
Step 5: Use Gradient Ribbon for Intensity
Brighter, more opaque ribbon indicates stronger absorption. Ribbon direction shows whether absorption is bullish (cyan) or bearish (magenta).
Step 6: Wait for Extreme Signals
Highest probability setups occur when "EXTREME ABSORPTION" or "EXTREME DISTRIBUTION" labels appear with 2+ timeframe confirmation.
Best Practices
Use on liquid instruments (major forex pairs, large-cap stocks, major crypto) for reliable signals
Absorption zones work best as reversal signals at price extremes
Combine with higher timeframe trend analysis - absorption against trend is lower probability
RVOL filter is critical - disable only on very low timeframe charts where volume is erratic
Oscillator gap confirmation adds significant edge - wait for "CONF" status when possible
Extreme signals (3/3 timeframe confirmation) have highest win rate but occur less frequently
Use gradient ribbon intensity to gauge absorption strength - brighter = stronger
Dashboard metrics provide context - high Signal Quality (>150%) indicates extreme deviation
Settings with all features turned on and with Continous VWAP Calculation instead of default Anchored Calculation & Deviation Method Z score:
Indicator Limitations
Requires sufficient volume data - may not work well on illiquid instruments or off-market hours
Multi-timeframe analysis requires data availability on all requested timeframes
VWAP deviation thresholds may need adjustment for different instruments and volatility regimes
Absorption zones indicate institutional activity but don't guarantee immediate reversal
False signals can occur during strong trending markets where institutions continue adding to positions
Oscillator gap confirmation adds lag - early signals may not have gap confirmation yet
Gradient ribbon visualization requires sufficient price movement to display properly
Dashboard metrics are real-time snapshots and can change rapidly during volatile periods
Technical Implementation
Built with Pine Script v6 using:
Multi-timeframe security requests with proper lookahead settings
Three VWAP deviation calculation methods (Price Volatility, Z-Score, Spread StDev)
Adaptive threshold system based on volatility regime and percentile analysis
Volume surge detection with customizable lookback and multiplier
RVOL filtering to eliminate low-liquidity false signals
Oscillator sigma gap calculation using Williams %R and CVD z-scores
9-layer gradient ribbon with progressive transparency
Real-time institutional dashboard with 8 key metrics
Signal aggregation across 2m, 5m, and 15m timeframes
The code is fully open-source and can be modified to suit individual trading styles.
Originality Statement
This indicator is original in its multi-timeframe institutional absorption detection approach. While VWAP deviation and volume analysis are established concepts, this indicator is justified because:
It combines VWAP deviation analysis across three timeframes with volume surge detection and RVOL filtering
The oscillator sigma gap confirmation provides unique divergence-based validation not found in standard VWAP indicators
Adaptive threshold system adjusts for volatility regime using percentile analysis
Signal aggregation requires 2+ timeframe confirmation, significantly reducing false signals
9-layer gradient ribbon provides intuitive visualization of absorption intensity
Institutional dashboard synthesizes multiple metrics (Order Flow, Liquidity Pressure, Absorption Strength, Signal Quality) into actionable intelligence
Integration of CVD z-score and Williams %R z-score for oscillator gap calculation is unique
Each component contributes unique information: VWAP deviation shows price extremes, volume surge shows institutional activity, RVOL filters noise, oscillator gap confirms divergence, timeframe alignment shows conviction, and the dashboard synthesizes all metrics. The indicator's value lies in presenting these complementary perspectives simultaneously with a unified absorption detection system.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
-Made with passion by officialjackofalltrades Indicator
Pressure Zone Analyzer [JOAT]Pressure Zone Analyzer
Introduction
The Pressure Zone Analyzer is an advanced open-source support/resistance indicator that combines dynamic pivot-based zone detection, Fibonacci level analysis, institutional level tracking, zone strength scoring, and multi-timeframe analysis into a comprehensive pressure zone intelligence system. This indicator helps traders identify where significant buying and selling pressure exists, where institutional levels act as magnets for price, and which zones have the highest probability of holding.
Unlike basic support/resistance indicators that draw static horizontal lines, this analyzer dynamically tracks pressure zones based on pivot points, calculates zone strength using volume, touches, and age, integrates Fibonacci golden zone analysis, monitors institutional weekly/daily levels, and provides real-time position assessment. The indicator is designed for traders who understand that not all support/resistance levels are equal and that zone quality determines trading success.
Why This Indicator Exists
This indicator addresses the challenge of identifying high-quality support and resistance zones in real-time. Markets respect some levels and ignore others. By systematically analyzing zone characteristics, this indicator reveals:
Dynamic Pressure Zones: Identifies support and resistance zones based on pivot points with automatic updates
Zone Strength Scoring: Calculates zone quality (0-100%) using volume, touch count, and age
Fibonacci Integration: Tracks key Fibonacci levels (23.6%, 38.2%, 50%, 61.8%, 78.6%) and golden zone (50-61.8%)
Institutional Levels: Monitors weekly and daily highs/lows that act as institutional reference points
Premium/Discount Zones: Identifies institutional buying zones (discount 0-30%) and selling zones (premium 70-100%)
Multi-Timeframe Analysis: Tracks higher timeframe levels for additional confluence
Position Assessment: Provides real-time analysis of price position relative to all zones
Each component provides different zone intelligence. Pivot-based zones show where price reversed, strength scoring shows zone quality, Fibonacci shows mathematical levels, institutional levels show reference points, premium/discount shows institutional bias, and position assessment shows current market context. Together, they create a comprehensive pressure zone system.
Core Components Explained
1. Dynamic Pivot-Based Zone Detection
Pressure zones are identified using pivot highs and lows:
float pivotHigh = ta.pivothigh(high, pivotLength, pivotLength)
float pivotLow = ta.pivotlow(low, pivotLength, pivotLength)
When a pivot high is detected, a resistance zone is created:
if not na(pivotHigh) and barstate.isconfirmed
PressureZone newZone = PressureZone.new()
newZone.zoneLine := line.new(bar_index - pivotLength, pivotHigh, bar_index + 50, pivotHigh,
color=resistanceColor, width=2, extend=extend.right)
newZone.price := pivotHigh
newZone.startBar := bar_index - pivotLength
newZone.zoneType := "resistance"
newZone.volumeAtZone := volume
Similarly for support zones with pivot lows. Zones are stored in arrays and automatically managed (old zones are removed when maximum count is reached).
Zone thickness is calculated as a percentage of price:
calcZoneThickness(float price, float thicknessPercent) =>
float thickness = price * (thicknessPercent / 100)
Default thickness is 0.5% of price, creating a zone rather than a single line. This accounts for the fact that support/resistance is a zone, not a precise price level.
2. Zone Strength Scoring System
Zone strength is calculated using three weighted components:
calcZoneStrength(int touches, float volAtZone, int age, float volWeight, float touchWeight, float ageWeight) =>
// Volume score (0-1)
float avgVolume = ta.sma(volume, 50)
float volScore = avgVolume > 0 ? math.min(volAtZone / avgVolume, 3.0) / 3.0 : 0.5
// Touch score (0-1)
float touchScore = math.min(touches / 5.0, 1.0)
// Age score (0-1) - newer zones score higher
float ageScore = math.max(1.0 - (age / 500.0), 0.0)
// Weighted combination
float strength = (volScore * volWeight) + (touchScore * touchWeight) + (ageScore * ageWeight)
Default weights:
Volume Weight: 40% - Higher volume at zone formation indicates institutional interest
Touch Weight: 30% - More touches indicate stronger zone
Age Weight: 30% - Newer zones are more relevant than old zones
Strength interpretation:
> 70%: Strong zone - high probability of holding
50-70%: Moderate zone - decent probability of holding
< 50%: Weak zone - lower probability of holding
The indicator tracks touches in real-time:
for zone in resistanceZones
if inZone(high, zone.price, thickness)
zone.touches += 1
zone.volumeAtZone := math.max(zone.volumeAtZone, volume)
Each touch increases zone strength, and high-volume touches increase it further.
3. Fibonacci Level Analysis
Fibonacci levels are calculated based on recent swing range:
calcFibLevels(float high, float low) =>
float priceRange = high - low
float fib236 = low + (priceRange * 0.236)
float fib382 = low + (priceRange * 0.382)
float fib500 = low + (priceRange * 0.500)
float fib618 = low + (priceRange * 0.618)
float fib786 = low + (priceRange * 0.786)
The indicator focuses on key levels:
50% (0.5): Equilibrium level - often acts as support/resistance
61.8% (0.618): Golden ratio - strongest Fibonacci level
Golden Zone is calculated as the area between 50% and 61.8%:
calcGoldenZone(float high, float low) =>
float priceRange = high - low
float goldenTop = low + (priceRange * 0.618)
float goldenBottom = low + (priceRange * 0.5)
The golden zone represents optimal entry area with best risk:reward ratio. Entries in the golden zone allow tight stops below 50% with targets at swing high.
4. Institutional Level Tracking
The indicator monitors key institutional reference levels:
Weekly High/Low:
float lastWeekHigh = request.security(syminfo.tickerid, "W", high ,
barmerge.gaps_off, barmerge.lookahead_off)
float lastWeekLow = request.security(syminfo.tickerid, "W", low ,
barmerge.gaps_off, barmerge.lookahead_off)
Daily High/Low:
float yesterdayHigh = request.security(syminfo.tickerid, "D", high ,
barmerge.gaps_off, barmerge.lookahead_off)
float yesterdayLow = request.security(syminfo.tickerid, "D", low ,
barmerge.gaps_off, barmerge.lookahead_off)
These levels act as magnets for price because:
Institutional algorithms reference these levels for order placement
Retail traders watch these levels for breakouts/breakdowns
Options and futures contracts often reference these levels
Previous day/week ranges provide context for current price action
5. Premium/Discount Zone System
Based on weekly range, the indicator calculates institutional bias zones:
float weekRange = lastWeekHigh - lastWeekLow
// Premium Zone (70-100% of range) - Institutional selling zone
float premiumTop = lastWeekHigh
float premiumBot = lastWeekLow + (weekRange * 0.7)
// Discount Zone (0-30% of range) - Institutional buying zone
float discountTop = lastWeekLow + (weekRange * 0.3)
float discountBot = lastWeekLow
// Golden Zone (50-61.8% of range) - Optimal entry zone
float goldenTop = lastWeekLow + (weekRange * 0.618)
float goldenBot = lastWeekLow + (weekRange * 0.5)
Trading logic:
In Discount Zone: Look for long entries - institutions are likely buying
In Premium Zone: Look for short entries - institutions are likely selling
In Golden Zone: Optimal risk:reward for entries in direction of trend
Between Zones: Neutral area - wait for price to reach discount or premium
This concept is based on institutional order flow: institutions buy in discount zones (value area) and sell in premium zones (overvalued area).
6. Multi-Timeframe Level Analysis
The indicator tracks higher timeframe levels for additional confluence:
float htfHigh = request.security(syminfo.tickerid, htfTimeframe, high ,
barmerge.gaps_off, barmerge.lookahead_off)
float htfLow = request.security(syminfo.tickerid, htfTimeframe, low ,
barmerge.gaps_off, barmerge.lookahead_off)
HTF timeframe is customizable (default: Daily). When current timeframe zones align with HTF levels, confluence increases zone strength.
7. Real-Time Position Assessment
The indicator continuously assesses price position:
// Check if in golden zone
bool inGoldenZone = close >= goldenBottom and close <= goldenTop
// Check if near resistance
bool nearResistance = false
for zone in resistanceZones
if inZone(close, zone.price, thickness * 2)
nearResistance := true
// Check if near support
bool nearSupport = false
for zone in supportZones
if inZone(close, zone.price, thickness * 2)
nearSupport := true
Position status:
AT RESISTANCE: Price near strong resistance zone - consider shorts or exits
AT SUPPORT: Price near strong support zone - consider longs or exits
GOLDEN ZONE: Price in optimal entry area - look for entries in trend direction
NEUTRAL: Price not near any significant zones - wait for better positioning
Visual Elements
Pressure Zone Lines: Horizontal lines showing resistance (red) and support (green) zones
Zone Strength Boxes: Filled boxes showing only strongest zones (strength > 60%) with strength percentage
Fibonacci Lines: Key Fibonacci levels (50% and 61.8%) with distinct colors
Golden Zone Fill: Shaded area between 50% and 61.8% Fibonacci levels
Institutional Lines: Weekly high/low (purple, thick) and Daily high/low (yellow, medium)
HTF Lines: Higher timeframe high/low (cyan) for additional confluence
Premium/Discount Fills: Shaded zones showing premium (red), discount (green), and golden (orange) areas
Position Markers: Visual alerts when price enters golden zone or approaches strong zones
Comprehensive Table: Dashboard showing top 2 resistance zones, top 2 support zones, institutional levels, Fibonacci levels, and current position status
Input Parameters
Pressure Zone Settings:
Zone Detection Length: Period for swing range calculation (default: 50, range: 20-200)
Pivot Length: Period for pivot detection (default: 10, range: 5-50)
Max Zones: Maximum zones to display (default: 8, range: 4-20)
Zone Thickness Percent: Zone width as percentage of price (default: 0.5%, range: 0.1-2.0%)
Fibonacci Settings:
Show Fibonacci Levels: Toggle Fib lines (default: enabled)
Show Golden Zone: Toggle golden zone fill (default: enabled)
Institutional Levels:
Show Last Week High/Low: Toggle weekly levels (default: enabled)
Show Yesterday High/Low: Toggle daily levels (default: enabled)
Strength Scoring:
Show Zone Strength: Toggle strength boxes (default: enabled)
Volume Weight: Weight for volume component (default: 0.4, range: 0.0-1.0)
Touch Weight: Weight for touch component (default: 0.3, range: 0.0-1.0)
Age Weight: Weight for age component (default: 0.3, range: 0.0-1.0)
Multi-Timeframe:
HTF Timeframe: Higher timeframe for level tracking (default: Daily)
Show HTF Levels: Toggle HTF lines (default: enabled)
Colors:
All colors are fully customizable including resistance, support, Fibonacci, golden zone, HTF levels, and institutional levels.
How to Use This Indicator
Step 1: Identify Strongest Zones
Look at the table to see top 2 resistance and support zones with strength percentages. Focus on zones with strength > 70%.
Step 2: Check Institutional Levels
Monitor weekly and daily highs/lows. These act as magnets for price and often provide strong support/resistance.
Step 3: Assess Premium/Discount Position
Determine if price is in premium zone (look for shorts), discount zone (look for longs), or golden zone (optimal entries).
Step 4: Look for Fibonacci Confluence
When pressure zones align with Fibonacci levels (especially 50% and 61.8%), zone strength increases significantly.
Step 5: Monitor Position Status
Check the table's position row. "AT RESISTANCE" or "AT SUPPORT" signals potential reversal or bounce areas.
Step 6: Wait for Zone Tests
Don't chase price. Wait for price to return to strong zones before entering. The best entries occur when price tests a zone and shows rejection.
Step 7: Use HTF Confluence
When current timeframe zones align with HTF levels, probability of zone holding increases. Look for these high-confluence areas.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal zone clarity
Focus on zones with strength > 70% - these have highest probability of holding
Multiple touches increase zone strength - zones that held before are likely to hold again
Golden zone entries offer best risk:reward - tight stops with large targets
Premium/discount zones work best in trending markets
Weekly levels are stronger than daily levels - prioritize weekly when they conflict
Wait for price to reach zones - don't anticipate, react
Look for volume confirmation when zones are tested - high volume rejections are strongest
Combine with price action - zones show where, price action shows when
HTF confluence significantly increases zone strength - prioritize these areas
Indicator Limitations
Zones don't always hold - even strong zones can break during major news or trend changes
Zone strength is relative to recent history - not absolute
Pivot-based detection requires sufficient price history - may not work on newly listed instruments
Maximum zone limits (8 default) mean some valid zones may not be displayed
Zone thickness is a percentage - may be too wide or narrow for some instruments
Premium/discount zones are relative to weekly range - not absolute value areas
Fibonacci levels are based on recent swing - may not align with longer-term structure
The indicator shows zones, not direction - requires trader interpretation
Works best on liquid instruments with clear support/resistance behavior
Zone strength scoring is a guide, not a guarantee - strong zones can still fail
Technical Implementation
Built with Pine Script v6 using:
Custom type definition for PressureZone with strength tracking
Array-based storage for resistance and support zones
Pivot-based zone detection with confirmation
Multi-component zone strength scoring
Touch and volume tracking for each zone
Fibonacci level calculations
Golden zone identification
Multi-timeframe security requests for institutional levels
Premium/discount zone calculations based on weekly range
Real-time position assessment
Dynamic table with 13 rows showing all metrics
Overlap prevention for visual clarity
Automatic zone cleanup when maximum count is reached
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its comprehensive pressure zone analysis. While individual components (pivot-based S/R, Fibonacci, institutional levels) are established concepts, this indicator is justified because:
It synthesizes five distinct zone analysis methodologies into a unified system
Zone strength scoring combines volume, touches, and age with customizable weights
Automatic zone management prevents clutter while highlighting strongest zones
Integration of Fibonacci golden zone with pivot-based zones
Premium/discount zone system based on institutional order flow concepts
Multi-timeframe level tracking for confluence analysis
Real-time position assessment provides actionable trading context
Comprehensive table shows all metrics simultaneously for holistic analysis
Overlap prevention ensures clean charts without sacrificing information
Each component contributes unique zone intelligence: pivot zones show where price reversed, strength scoring shows zone quality, Fibonacci shows mathematical levels, institutional levels show reference points, premium/discount shows institutional bias, HTF levels show confluence, and position assessment shows current context. The indicator's value lies in presenting these complementary perspectives simultaneously with quantitative strength scoring and intelligent display management.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Pressure zone analysis is a tool for identifying potential support and resistance areas, not a crystal ball for predicting future price movement. Strong zones, high strength scores, and institutional levels do not guarantee profitable trades. Past zone behavior does not guarantee future zone behavior. Market conditions change, and strategies that worked historically may not work in the future.
The zones and levels displayed are mathematical calculations based on current market data, not predictions of future price movement. High-strength zones can break, golden zone entries can fail, and institutional levels can be violated. Users must conduct their own analysis and risk assessment before making trading decisions.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Regime Classification System [JOAT]Regime Classification System
Introduction
The Regime Classification System is an advanced open-source market regime detection indicator that combines smooth range filtering, multi-timeframe trend analysis (10 timeframes), impulse detection, Chandelier Exit integration, and regime strength scoring into a comprehensive market state classification system. This indicator helps traders identify whether the market is trending, ranging, volatile, or transitioning between states, enabling them to adapt their trading strategies to current market conditions.
Unlike basic trend indicators that simply show up or down, this system classifies markets into distinct regimes (Trend Bull, Trend Bear, Volatile Bull, Volatile Bear, High Vol Range, Low Vol Range, Flat) and provides confidence metrics, regime strength scores, multi-timeframe alignment analysis, and transition warnings. The indicator is designed for traders who understand that different market conditions require different trading approaches and that regime identification is critical for consistent profitability.
Why This Indicator Exists
This indicator addresses a fundamental challenge in trading: adapting strategy to market conditions. A trend-following strategy that works in trending markets fails in ranging markets. A mean-reversion strategy that works in ranging markets fails in trending markets. By systematically classifying market regimes, this indicator enables traders to:
Identify Current Regime: Classify market as trending, ranging, volatile, or flat with quantitative metrics
Measure Regime Strength: Score regime quality (0-100%) based on trend clarity, volatility consistency, impulse confirmation, and duration
Detect Regime Transitions: Warn when market is likely changing character before it becomes obvious
Analyze Multi-Timeframe Alignment: Confirm regime across 10 timeframes (1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, Daily, Weekly)
Calculate Regime Confidence: Provide confidence score combining regime strength, MTF alignment, and transition probability
Integrate Dynamic Stops: Use Chandelier Exit for adaptive stop-loss placement based on volatility
Each component provides different regime intelligence. Range filtering shows directional movement, trend strength shows conviction, volatility ratio shows market character, impulse detection shows momentum, MTF alignment shows multi-timeframe conviction, and Chandelier Exit provides dynamic risk management. Together, they create a comprehensive regime classification system.
Core Components Explained
1. Smooth Range Filter (from RealGains Algorithm)
The range filter uses a sophisticated smoothing algorithm to identify directional movement:
// Smooth range calculation
smoothrng(x, t, m) =>
wper = t * 2 - 1
avrng = ta.ema(math.abs(x - x ), t)
smoothrng = ta.ema(avrng, wper) * m
// Range filter
rngfilt(x, r) =>
rngfilt = x
rngfilt := x > nz(rngfilt ) ? x - r < nz(rngfilt ) ? nz(rngfilt ) : x - r :
x + r > nz(rngfilt ) ? nz(rngfilt ) : x + r
The filter creates upper and lower bands based on smoothed range. When price breaks above the filter, it signals upward movement. When price breaks below, it signals downward movement. The filter adapts to volatility, widening in volatile conditions and tightening in calm conditions.
Filter direction is tracked using consecutive bar counts:
upward = filt > filt ? nz(upward ) + 1 : 0
downward = filt < filt ? nz(downward ) + 1 : 0
Longer consecutive counts indicate stronger directional conviction.
2. Impulse Detection (SMMA and ZLEMA)
The indicator uses Smoothed Moving Average (SMMA) and Zero-Lag EMA (ZLEMA) to detect impulse moves:
// SMMA calculation
calc_smma(src, len) =>
var float smma = na
smma := na(smma) ? ta.sma(src, len) : (smma * (len - 1) + src) / len
// ZLEMA calculation
calc_zlema(src, len) =>
ema1 = ta.ema(src, len)
ema2 = ta.ema(ema1, len)
d = ema1 - ema2
ema1 + d
// Impulse detection
hi = calc_smma(high, 34)
lo = calc_smma(low, 34)
mi = calc_zlema(hlc3, 34)
md = mi > hi ? mi - hi : mi < lo ? mi - lo : 0
is_impulse = md != 0
When impulse is detected, the market has momentum. When impulse is absent (flat), the market lacks directional conviction. This helps filter out choppy, directionless periods.
3. Trend Strength Calculation (ADX-based)
The indicator calculates trend strength using Directional Movement Index (DMI) and Average Directional Index (ADX):
calcTrendStrength(int length) =>
float plusDM = high - high > low - low ? math.max(high - high , 0) : 0
float minusDM = low - low > high - high ? math.max(low - low, 0) : 0
float plusDI = atr > 0 ? ta.sma(plusDM, length) / atr * 100 : 0
float minusDI = atr > 0 ? ta.sma(minusDM, length) / atr * 100 : 0
float dx = math.abs(plusDI - minusDI) / (plusDI + minusDI) * 100
float adx = ta.sma(dx, length)
float trendStrength = adx / 100
bool bullish = plusDI > minusDI
Trend strength ranges from 0 (no trend) to 1 (strong trend). The threshold (default: 0.6) determines when a market is classified as trending vs ranging.
4. Volatility Regime Classification
Volatility regime is determined by comparing current ATR to average ATR:
calcVolatilityRegime(int length) =>
float atr = ta.atr(length)
float atrMA = ta.sma(atr, length)
float volRatio = atrMA > 0 ? atr / atrMA : 1.0
Volatility ratio interpretation:
volRatio > 1.5: High volatility (default threshold)
volRatio 0.67-1.5: Normal volatility
volRatio < 0.67: Low volatility
High volatility regimes require wider stops and larger profit targets. Low volatility regimes allow tighter stops and smaller targets.
5. Regime Classification Logic
The indicator combines trend strength, volatility ratio, and impulse detection to classify regimes:
classifyRegime(float trendStr, bool isBullish, float volRatio, float threshold, float volThresh, bool impulse) =>
if not impulse and catchFlat
regime := "Flat"
else if trendStr >= threshold
if volRatio > volThresh
regime := isBullish ? "Volatile Bull" : "Volatile Bear"
else
regime := isBullish ? "Trend Bull" : "Trend Bear"
else
if volRatio > volThresh
regime := "High Vol Range"
else
regime := "Low Vol Range"
Regime classifications:
Trend Bull: Strong uptrend with normal volatility - trend-following strategies
Trend Bear: Strong downtrend with normal volatility - trend-following strategies
Volatile Bull: Uptrend with high volatility - wider stops, larger targets
Volatile Bear: Downtrend with high volatility - wider stops, larger targets
High Vol Range: No clear trend with high volatility - avoid or use wide ranges
Low Vol Range: No clear trend with low volatility - mean-reversion strategies
Flat: No impulse detected - avoid trading
6. Regime Strength Scoring (0-100%)
Regime strength is calculated using four components:
calcRegimeStrength(float trendStr, float volRatio, bool impulse, int barsInRegime) =>
// Component 1: Trend clarity (40 points)
float trendScore = trendStr * 40
// Component 2: Volatility consistency (20 points)
float volScore = volRatio < volThreshold ? 20 : math.max(0, 20 - (volRatio - volThreshold) * 10)
// Component 3: Impulse confirmation (20 points)
float impulseScore = impulse ? 20 : 0
// Component 4: Regime duration (20 points)
float durationScore = math.min(barsInRegime / 50.0, 1.0) * 20
float totalScore = trendScore + volScore + impulseScore + durationScore
Regime strength interpretation:
> 70%: Excellent regime - high confidence trades
40-70%: Good regime - moderate confidence trades
< 40%: Weak regime - low confidence or avoid
7. Regime Transition Detection
The indicator warns when regime is likely changing:
detectRegimeTransition(float trendStr, float volRatio, bool impulse) =>
bool weakTrend = trendStr < trendThreshold * 0.8
bool volSpike = volRatio > volThreshold * 1.5
bool lostImpulse = not impulse and catchFlat
float transitionProb = 0.0
if weakTrend
transitionProb += 40
if volSpike
transitionProb += 30
if lostImpulse
transitionProb += 30
bool inTransition = transitionProb >= 50
Transition warnings help traders exit positions before regime changes become obvious in price.
8. Multi-Timeframe Alignment (10 Timeframes)
The indicator analyzes regime across 10 timeframes:
= request.security(syminfo.tickerid, '1', get_trend_status())
= request.security(syminfo.tickerid, '3', get_trend_status())
= request.security(syminfo.tickerid, '5', get_trend_status())
= request.security(syminfo.tickerid, '15', get_trend_status())
= request.security(syminfo.tickerid, '30', get_trend_status())
= request.security(syminfo.tickerid, '60', get_trend_status())
= request.security(syminfo.tickerid, '120', get_trend_status())
= request.security(syminfo.tickerid, '240', get_trend_status())
= request.security(syminfo.tickerid, 'D', get_trend_status())
= request.security(syminfo.tickerid, 'W', get_trend_status())
MTF alignment score is calculated with weighted timeframes (higher timeframes have more weight):
calcMTFAlignment(string t1m, string t5m, string t15m, string t1h, string t4h, string tD) =>
int bullCount = 0
int bearCount = 0
// Count each timeframe with weights
// 1m, 5m, 15m: weight 1
// 1h: weight 2
// 4h: weight 3
// Daily: weight 4
float alignmentScore = (bullCount - bearCount) / totalCount * 100
Alignment interpretation:
> 60: Strong Bull alignment
30-60: Moderate Bull alignment
-30 to 30: Mixed alignment
-60 to -30: Moderate Bear alignment
< -60: Strong Bear alignment
9. Regime Confidence Calculation
Overall confidence combines regime strength, MTF alignment, and transition status:
calcRegimeConfidence(float regimeStrength, float alignmentScore, bool inTransition) =>
float confidence = regimeStrength
// Adjust for alignment
float alignmentBonus = math.abs(alignmentScore) / 100 * 20
confidence += alignmentBonus
// Penalize if in transition
if inTransition
confidence *= 0.5
confidence := math.min(confidence, 100)
Confidence > 70% indicates high-quality regime suitable for aggressive trading. Confidence < 40% suggests caution or avoiding trades.
10. Chandelier Exit Integration
The indicator includes Chandelier Exit for dynamic stop-loss placement:
atrCE = ceMult * ta.atr(ceLength)
longStop = (ceUseClose ? ta.highest(close, ceLength) : ta.highest(ceLength)) - atrCE
shortStop = (ceUseClose ? ta.lowest(close, ceLength) : ta.lowest(ceLength)) + atrCE
Chandelier Exit adapts to volatility, providing wider stops in volatile regimes and tighter stops in calm regimes. The stops trail price, locking in profits as trends develop.
Visual Elements
Range Filter Line: Main line showing directional filter with color-coded regime (green = bull, red = bear, cyan = neutral)
Target Bands: Upper and lower bands showing filter range with gradient fills
Regime Strength Zones: Gradient fills showing regime strength intensity
Volatility Expansion Zones: Circles marking high volatility periods
Chandelier Exit Lines: Dynamic stop-loss lines (green for long stops, red for short stops)
Regime Value Histogram: Histogram showing regime direction and strength (-3 to +3)
Regime Background: Subtle background coloring based on current regime
Regime Change Markers: Circles marking regime transitions
Transition Warnings: X-crosses marking potential regime changes
Regime Signals: Triangle markers for strong bull/bear regime confirmations
MTF Table: Comprehensive table showing all 10 timeframes with trend status
Statistics Panel: Additional metrics including regime strength, duration, alignment, confidence, and transition status
Input Parameters
Range Filter Settings:
Sampling Period: Period for range calculation (default: 100, range: 1+)
Range Multiplier: Multiplier for range width (default: 3.0, range: 0.1+)
Regime Detection:
Trend Threshold: Minimum trend strength for trending classification (default: 0.6, range: 0.3-0.9)
Volatility Threshold: Multiplier for high volatility classification (default: 1.5, range: 1.0-3.0)
Regime Strength Period: Period for strength calculations (default: 20, range: 5-100)
Show Regime Signals: Toggle regime confirmation markers (default: enabled)
Chandelier Exit:
Chandelier ATR Period: Period for ATR calculation (default: 22, range: 1+)
Chandelier ATR Multiplier: Multiplier for stop distance (default: 3.0, range: 0.1+)
Use Close for Extremums: Use close vs high/low for calculations (default: enabled)
Impulse Detection:
Try to Catch Flat: Enable flat regime detection (default: enabled)
Multi-Timeframe Table:
Show MTF Table: Toggle timeframe table (default: enabled)
Table Position: Dashboard location (Top Right/Top Left/Bottom Right/Bottom Left/Middle Right)
Show Regime Statistics: Toggle additional statistics panel (default: enabled)
Colors:
All colors are fully customizable including trend bull/bear, mid trend, range, high volatility, text, transition, and excellent regime colors.
How to Use This Indicator
Step 1: Identify Current Regime
Check the regime classification (Trend Bull, Trend Bear, Volatile Bull, Volatile Bear, High Vol Range, Low Vol Range, Flat). This determines your trading approach.
Step 2: Check Regime Strength
Look at regime strength percentage. > 70% indicates high-quality regime suitable for aggressive trading. < 40% suggests caution.
Step 3: Verify MTF Alignment
Check the MTF table. Strong alignment (> 60) across multiple timeframes confirms regime conviction. Mixed alignment suggests caution.
Step 4: Monitor Regime Confidence
Overall confidence score combines strength, alignment, and transition status. > 70% confidence indicates high-quality trading conditions.
Step 5: Watch for Transition Warnings
X-cross markers warn of potential regime changes. Consider tightening stops or exiting positions when transition probability is high.
Step 6: Use Chandelier Exit for Stops
The Chandelier Exit lines provide dynamic stop-loss levels that adapt to volatility. Trail stops as trends develop.
Step 7: Adapt Strategy to Regime
Trend Bull/Bear: Use trend-following strategies, ride trends, trail stops
Volatile Bull/Bear: Use wider stops, larger targets, reduce position size
High Vol Range: Avoid or use very wide ranges
Low Vol Range: Use mean-reversion strategies, fade extremes
Flat: Avoid trading, wait for impulse to return
Best Practices
Use on 15-minute to 4-hour timeframes for optimal regime clarity
Trade with the regime, not against it - trend-following in trending regimes, mean-reversion in ranging regimes
Higher regime strength = higher confidence = larger position sizes
MTF alignment is critical - don't trade against higher timeframe regimes
Transition warnings are early signals - tighten stops or exit before regime change becomes obvious
Chandelier Exit provides objective stop-loss levels - use them
Regime duration matters - longer regimes are more reliable
Confidence > 70% = aggressive trading, confidence < 40% = defensive or avoid
Flat regimes lack directional conviction - patience is key
Volatile regimes require wider stops and larger targets - adjust risk accordingly
Indicator Limitations
Regime classification is based on recent data - sudden news events can invalidate regimes instantly
Transition warnings are probabilistic, not guaranteed - regimes can persist longer than expected
MTF alignment requires sufficient data on all timeframes - may not work on newly listed instruments
Range filter is adaptive but can lag during rapid regime changes
Impulse detection can produce false flat signals during consolidation within trends
Regime strength scoring is relative to recent history - not absolute
Chandelier Exit can be stopped out during volatile whipsaws
The indicator identifies regimes but doesn't predict when they will end
Works best on liquid instruments with clear trending and ranging periods
Regime confidence is a guide, not a guarantee - high confidence regimes can still fail
Technical Implementation
Built with Pine Script v6 using:
Smooth range filter with adaptive volatility adjustment
SMMA and ZLEMA calculations for impulse detection
ADX-based trend strength calculations
ATR-based volatility regime classification
Multi-component regime strength scoring
Transition probability calculations
Multi-timeframe security requests (10 timeframes)
Weighted MTF alignment scoring
Regime confidence calculations
Chandelier Exit with trailing stops
Dynamic table with 17 rows showing all timeframes and statistics
Gradient fills and color-coded visualizations
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its comprehensive regime classification approach. While individual components (range filter, ADX, Chandelier Exit) are established concepts, this indicator is justified because:
It synthesizes six distinct regime analysis methodologies into a unified classification system
Regime strength scoring combines trend clarity, volatility consistency, impulse confirmation, and duration
Transition detection provides early warnings before regime changes become obvious
MTF alignment analysis across 10 timeframes with weighted scoring
Regime confidence calculation integrates strength, alignment, and transition probability
Integration of Chandelier Exit provides regime-adaptive risk management
Comprehensive statistics panel shows regime quality metrics in real-time
Visual regime signals help traders identify high-quality trading conditions
Each component contributes unique regime intelligence: range filter shows direction, trend strength shows conviction, volatility ratio shows character, impulse shows momentum, MTF alignment shows multi-timeframe conviction, transition detection shows regime changes, and Chandelier Exit provides adaptive stops. The indicator's value lies in presenting these complementary perspectives simultaneously with quantitative regime classification and confidence scoring.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Regime classification is a tool for understanding market conditions, not a crystal ball for predicting future price movement. High regime strength, strong MTF alignment, and high confidence scores do not guarantee profitable trades. Past regime patterns do not guarantee future regime patterns. Market conditions change, and strategies that worked historically may not work in the future.
The metrics displayed are mathematical calculations based on current market data, not predictions of future price movement. Transition warnings are probabilistic, not guaranteed. Chandelier Exit stops can be hit during volatile whipsaws. Users must conduct their own analysis and risk assessment before making trading decisions.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Institutional Structure Intelligence Engine [JOAT]Institutional Structure Intelligence Engine
Introduction
The Institutional Structure Intelligence Engine is an advanced open-source market structure indicator that combines swing detection, order block analysis, fair value gap (FVG) identification, institutional level tracking, and velocity analysis into a comprehensive structural intelligence system. This indicator helps traders identify where institutional orders are positioned, where price inefficiencies exist, and how market structure is evolving in real-time.
Unlike basic support/resistance indicators that draw static lines, this engine dynamically tracks institutional footprints through order blocks (zones where institutions accumulated or distributed), fair value gaps (price inefficiencies that often get filled), breaker blocks (failed order blocks signaling reversals), and multi-timeframe institutional levels. The indicator is designed for traders who understand that market structure reveals institutional intent and that price gravitates toward areas of institutional interest.
Why This Indicator Exists
This indicator addresses the challenge of identifying institutional positioning in real-time. Institutional traders leave structural footprints that can be detected through systematic analysis. By combining multiple structural methodologies, this indicator reveals:
Order Block Detection: Identifies zones where institutions accumulated or distributed positions before major moves
Fair Value Gaps: Detects price inefficiencies where rapid institutional movement left unfilled gaps
Breaker Blocks: Tracks failed order blocks that signal potential trend reversals
Institutional Levels: Monitors Weekly/Daily highs and lows, Premium/Discount zones, and Golden Zone (0.618-0.5 Fibonacci)
Structure Velocity: Measures how quickly market structure is forming to identify momentum shifts
Compression Zones: Detects periods of range compression that often precede explosive moves
Each component provides a different structural perspective. Order blocks show where institutions positioned, FVGs show where price moved inefficiently, breaker blocks show where structure failed, institutional levels show key reference points, velocity shows momentum, and compression zones show coiling energy. Together, they create a comprehensive structural intelligence system.
Below showing the Main Features and how it works:
Core Components Explained
1. Advanced Order Block Detection
Order blocks are identified using strict volume and price action criteria:
Bullish Order Block:
// Two consecutive down candles followed by strong up move
if close < open and close < open and close > open and
volume > avgVolume * obVolumeThreshold and
close > high and
(high - low ) <= atr * maxATRMult and
(close - open) > atr * 0.5
Bearish Order Block:
// Two consecutive up candles followed by strong down move
if close > open and close > open and close < open and
volume > avgVolume * obVolumeThreshold and
close < low and
(high - low ) <= atr * maxATRMult and
(open - close) > atr * 0.5
Order blocks represent the last opposite-direction move before a strong impulse. The logic: institutions accumulate/distribute in the opposite direction before pushing price in their intended direction. The indicator tracks:
Order block volume (total volume during formation)
Number of touches (how many times price returned to the zone)
Zone strength (calculated from volume, touches, and age)
Breaker status (whether the order block was invalidated)
Overlapping Order Block Combination:
When multiple order blocks overlap, the indicator combines them into a single stronger zone:
if doOBsOverlap(ob1, ob2)
ob1.top := math.max(ob1.top, ob2.top)
ob1.bottom := math.min(ob1.bottom, ob2.bottom)
ob1.obVolume += ob2.obVolume
ob1.touches += ob2.touches
ob1.strength := math.max(ob1.strength, ob2.strength)
This prevents chart clutter and highlights the most significant institutional zones.
2. Breaker Block Detection
Breaker blocks are order blocks that failed - price broke through them instead of bouncing. This signals potential trend reversal:
// Bullish OB becomes breaker if price breaks below
if low < ob.bottom
ob.breaker := true
ob.breakTime := time
// Bearish OB becomes breaker if price breaks above
if high > ob.top
ob.breaker := true
ob.breakTime := time
Breaker blocks are displayed with distinct colors (cyan for bullish breakers, orange for bearish breakers) to differentiate them from active order blocks. When an order block becomes a breaker, it often signals that institutional positioning has changed and the previous structure is no longer valid.
3. Fair Value Gap (FVG) Detection
FVGs are identified using strict gap and volume criteria:
Bullish FVG:
// Gap between 2 bars ago high and current low
bool bullishFVGDetected = low > high and
(low - high ) > atr * 0.3 and // Minimum gap size
volume > avgVolume * 0.8 // Volume confirmation
Bearish FVG:
// Gap between 2 bars ago low and current high
bool bearishFVGDetected = high < low and
(low - high) > atr * 0.3 and // Minimum gap size
volume > avgVolume * 0.8 // Volume confirmation
FVGs represent price inefficiencies where institutional orders moved price so quickly that normal auction process was bypassed. These gaps often get "filled" as price returns to establish fair value. The indicator tracks:
FVG top and bottom prices
Mitigation status (whether the gap has been filled)
Mitigation bar (when the gap was filled)
Only non-mitigated FVGs are displayed to keep charts clean. Maximum FVG count is customizable (default: 3) to prevent clutter.
Showing Order Block, Breaker Block, and All Combined OB's that occured:
4. Institutional Level Tracking
The indicator monitors key institutional reference levels:
Weekly High/Low:
float lastWeekHigh = request.security(syminfo.tickerid, "W", high )
float lastWeekLow = request.security(syminfo.tickerid, "W", low )
Daily High/Low:
float yesterdayHigh = request.security(syminfo.tickerid, "D", high )
float yesterdayLow = request.security(syminfo.tickerid, "D", low )
Premium/Discount Zones:
Based on weekly range:
Premium Zone: 70%-100% of weekly range (institutional selling zone)
Discount Zone: 0%-30% of weekly range (institutional buying zone)
Golden Zone: 50%-61.8% of weekly range (optimal entry zone)
float weekRange = lastWeekHigh - lastWeekLow
float premiumTop = lastWeekHigh
float premiumBot = lastWeekLow + (weekRange * 0.7)
float discountTop = lastWeekLow + (weekRange * 0.3)
float discountBot = lastWeekLow
float goldenTop = lastWeekLow + (weekRange * 0.618)
float goldenBot = lastWeekLow + (weekRange * 0.5)
These zones help traders identify where institutions are likely to buy (discount) or sell (premium), with the golden zone representing optimal risk:reward entries.
Breaker Block with VOL, Discount zone touched for signal, Market Phase + Quality of chart score:
5. Structure Velocity Analysis
The indicator measures how quickly market structure is forming:
// Price velocity
priceVelocity = ta.change(close, velocityLength) / velocityLength
velocityMA = ta.sma(math.abs(priceVelocity), velocityLength)
velocityScore = velocityMA > 0 ? math.abs(priceVelocity) / velocityMA : 0
// Volume momentum
volumeMomentum = volume / avgVolume
volumeAcceleration = ta.change(volumeMomentum, 5)
// Structure velocity (how fast structure is forming)
structureVelocity = (bar_index - lastSwingHighBar) + (bar_index - lastSwingLowBar)
High velocity indicates rapid structure formation (trending market), low velocity indicates slow structure formation (ranging market). Velocity analysis helps traders identify momentum shifts before they become obvious in price.
6. Compression to Expansion Detection
The indicator detects periods of range compression using strict criteria:
float rangeMA = ta.sma(high - low, 50)
float currentRange = high - low
bool compressed = currentRange < rangeMA * 0.3 and volume < avgVolume * 0.8
bool expanding = currentRange > rangeMA * 2.0 and volume > avgVolume * 1.3
Compression zones are only displayed if:
Compression lasted at least 10 bars
Range is less than 1.5x ATR (truly tight)
This prevents false compression signals and highlights only significant coiling periods that often precede explosive moves.
7. Swing Point Detection
The indicator uses pivot-based swing detection:
pivotHigh = ta.pivothigh(high, swingLength, swingLength)
pivotLow = ta.pivotlow(low, swingLength, swingLength)
Swing points are stored in arrays and used for:
Structure line drawing
Break of Structure (BOS) detection
Change of Character (CHOCH) detection
Trend determination
Swing length is customizable (default: 10) to adjust sensitivity.
Visual Elements
Order Block Boxes: Filled boxes showing bullish (green) and bearish (red) order blocks with volume and touch count
Breaker Block Boxes: Distinct colored boxes (cyan/orange) showing failed order blocks
FVG Boxes: Transparent boxes showing bullish (green) and bearish (red) fair value gaps
Institutional Lines: Weekly high/low (purple), Daily high/low (yellow)
Premium/Discount Fills: Shaded zones showing premium (red), discount (green), and golden (orange) zones
Compression Boxes: Purple boxes showing range compression periods
Swing Points: Triangle markers showing swing highs (red) and swing lows (green)
All visual elements use "locked" boxes that don't extend indefinitely, preventing chart clutter. Overlap prevention logic ensures boxes don't stack on top of each other.
Input Parameters
Structure Detection:
Swing Length: Period for pivot detection (default: 10, range: 3-50)
Show Swing Points: Toggle swing markers (default: enabled)
Show Structure Lines: Toggle structure lines (default: enabled)
Show Compression Zones: Toggle compression boxes (default: disabled to reduce clutter)
Order Blocks:
Show Order Blocks: Toggle order block boxes (default: enabled)
Combine Overlapping OBs: Merge overlapping order blocks (default: enabled)
Show Breaker Blocks: Toggle breaker block display (default: enabled)
Volume Threshold: Minimum volume multiplier for OB detection (default: 1.5)
Max Order Blocks: Maximum OBs to display (default: 3, range: 1-10)
Max ATR Multiplier: Maximum OB size relative to ATR (default: 2.5)
Market Structure:
Show Break of Structure: Toggle BOS markers (default: disabled to reduce clutter)
Show Change of Character: Toggle CHOCH markers (default: enabled)
Show Fair Value Gaps: Toggle FVG boxes (default: enabled)
Show FVG Mitigation: Track when FVGs are filled (default: enabled)
Max FVGs to Display: Maximum FVGs to show (default: 3, range: 1-10)
Institutional Levels:
Show Weekly High/Low: Toggle weekly levels (default: enabled)
Show Daily High/Low: Toggle daily levels (default: enabled)
Show Golden Zone: Toggle 0.618-0.5 Fib zone (default: enabled)
Show Premium/Discount Zones: Toggle institutional zones (default: enabled)
Velocity Analysis:
Show Structure Velocity: Toggle velocity calculations (default: enabled)
Velocity Period: Period for velocity analysis (default: 20, range: 5-50)
Display:
Table Position: Dashboard location (Top Right/Top Left/Bottom Right/Bottom Left)
Show Structure Quality Score: Toggle quality metrics (default: enabled)
Colors:
All colors are fully customizable including bullish/bearish structure, order blocks, breaker blocks, FVGs, weekly/daily levels, golden zone, premium/discount zones, and compression zones.
4HR TF BTCUSDT showing the zones being used in action and price movement:
How to Use This Indicator
Step 1: Identify Key Institutional Zones
Look for order blocks with high touch counts and strong volume. These represent areas where institutions are likely to defend their positions.
Step 2: Monitor Fair Value Gaps
FVGs often get filled as price returns to establish fair value. Look for entries when price approaches unfilled FVGs, especially if they align with order blocks.
Step 3: Watch for Breaker Blocks
When an order block becomes a breaker, it signals that institutional positioning has changed. This often marks trend reversals or significant structure shifts.
Step 4: Use Premium/Discount Zones
Look for long entries in discount zones (0-30% of range) and short entries in premium zones (70-100% of range). The golden zone (50-61.8%) offers optimal risk:reward.
Step 5: Check Institutional Levels
Weekly and daily highs/lows act as magnets for price. Breaks above/below these levels often lead to significant moves.
Step 6: Monitor Structure Velocity
High velocity indicates trending conditions (follow the trend), low velocity indicates ranging conditions (fade extremes).
Step 7: Wait for Compression Breakouts
Compression zones mark periods of coiling energy. Breakouts from compression often lead to explosive moves with strong follow-through.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal structure clarity
Combine order blocks with FVGs for high-probability entries
Wait for price to return to order blocks before entering - don't chase
Breaker blocks often become new support/resistance in opposite direction
Premium/discount zones work best in trending markets
Golden zone entries offer best risk:reward when combined with order blocks
Compression zones require patience - wait for confirmed breakout
Structure velocity helps determine whether to trade with trend or fade extremes
Multiple touches on an order block increase its significance
FVG fills often provide excellent entry opportunities with tight stops
Indicator Limitations
Order blocks don't always hold - institutions can change positioning
FVGs don't always get filled - some gaps persist indefinitely
Breaker blocks can fail - price can return above/below breaker zones
Premium/discount zones are relative to recent range - not absolute levels
Compression detection requires sufficient bars - may not work on new instruments
Structure velocity is a lagging indicator - confirms moves after they start
Maximum box/line limits (500 each) can be reached on lower timeframes with long history
Overlap prevention may hide some valid order blocks to prevent clutter
The indicator shows structure, not direction - requires trader interpretation
Works best on liquid instruments with clear institutional participation
Technical Implementation
Built with Pine Script v6 using:
Custom type definitions for OrderBlockInfo and FVGInfo
Array-based storage for order blocks, FVGs, and swing points
Strict volume and ATR-based filtering for accuracy
Overlap detection and combination logic for order blocks
Breaker block tracking with time-based invalidation
FVG mitigation detection
Multi-timeframe security requests for institutional levels
Fibonacci-based premium/discount zone calculations
Velocity and momentum analysis
Compression detection with strict criteria
Dynamic box and label management with anti-overlap logic
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its comprehensive structural integration. While individual components (order blocks, FVGs, institutional levels) are established concepts, this indicator is justified because:
It combines seven distinct structural methodologies into a unified intelligence system
Order block detection uses strict multi-criteria filtering (volume, ATR, price action) for accuracy
Automatic order block combination prevents clutter while highlighting strongest zones
Breaker block tracking provides reversal signals not available in basic order block indicators
FVG detection includes mitigation tracking and strict size/volume filtering
Premium/discount zones integrate Fibonacci analysis with institutional levels
Structure velocity analysis provides momentum context for structural zones
Compression detection uses strict criteria to identify only significant coiling periods
Anti-overlap logic ensures clean charts without sacrificing information
Each component contributes unique structural intelligence: order blocks show institutional positioning, FVGs show inefficiencies, breaker blocks show failures, institutional levels show reference points, velocity shows momentum, and compression shows coiling energy. The indicator's value lies in presenting these complementary structural perspectives simultaneously with intelligent filtering and display management.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Market structure analysis is a tool for understanding institutional positioning, not a crystal ball for predicting future price movement. Order blocks, FVGs, and institutional levels do not guarantee profitable trades. Past structural patterns do not guarantee future structural patterns. Market conditions change, and strategies that worked historically may not work in the future.
The zones and levels displayed are mathematical calculations based on current market data, not predictions of future price movement. High-quality order blocks, unfilled FVGs, and premium/discount zones do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Institutional Liquidity Flow Engine [JOAT]Institutional Liquidity Flow Engine
Introduction
The Institutional Liquidity Flow Engine is an advanced open-source volume analysis indicator that combines relative volume monitoring, buyer/seller strength analysis, multi-timeframe alignment detection, and comprehensive flow metrics into a unified institutional-grade tool. This indicator helps traders identify when institutional money is entering or exiting positions by analyzing volume patterns, pressure dynamics, and liquidity conditions across multiple timeframes.
Unlike basic volume indicators that simply show volume bars, this engine dissects volume into actionable intelligence: relative volume (RVOL) to identify unusual activity, buyer/seller strength ratios to determine who controls the market, accumulation/distribution trends to track smart money positioning, and multi-timeframe alignment to confirm directional conviction. The indicator is designed for traders who understand that volume precedes price and that institutional footprints can be detected through systematic volume analysis.
Why This Indicator Exists
This indicator addresses a critical gap in retail trading: the ability to detect institutional activity in real-time. Institutional traders move large positions that create detectable volume signatures. By combining multiple volume analysis methodologies, this indicator reveals:
Relative Volume Analysis: Identifies when volume is significantly above or below average, signaling potential institutional activity
Buyer/Seller Strength: Quantifies the balance of power between buyers and sellers using volume-weighted calculations
Multi-Timeframe Alignment: Confirms whether volume patterns align across 1m, 5m, 15m, 30m, 1h, 2h, 4h, Daily, and Weekly timeframes
Flow Metrics: Tracks Money Flow Index (MFI), On-Balance Volume (OBV), Accumulation/Distribution (A/D), and VWAP deviation
Liquidity Classification: Categorizes market conditions as Strong Buying, Strong Selling, Balanced, or Thin liquidity
Each component provides a different lens on volume behavior. RVOL shows intensity, buyer/seller strength shows direction, MTF alignment shows conviction, flow metrics show institutional positioning, and liquidity classification shows market conditions. Together, they create a comprehensive view of institutional activity.
Core Components Explained
1. Relative Volume (RVOL) Analysis
RVOL is calculated as current volume divided by the average volume over a specified period (default 20 bars):
avgVolume = ta.sma(volume, volumeLength)
relativeVolume = avgVolume > 0 ? volume / avgVolume : 1.0
The indicator classifies RVOL into five categories:
Extreme (RVOL >= 3.0): Institutional-level activity, potential climax moves
High (RVOL >= 1.5): Above-average activity, significant interest
Normal (RVOL >= 1.0): Average activity, typical market conditions
Low (RVOL >= 0.5): Below-average activity, reduced interest
Very Low (RVOL < 0.5): Minimal activity, thin liquidity
RVOL thresholds are customizable. Higher RVOL often precedes significant price moves as institutions accumulate or distribute positions.
2. Buyer/Seller Strength Analysis
The indicator calculates buyer and seller strength using volume-weighted analysis:
buyerVolume = close > open ? volume : 0
sellerVolume = close < open ? volume : 0
buyerStrength = ta.sma(buyerVolume, volumeLength)
sellerStrength = ta.sma(sellerVolume, volumeLength)
Strength ratios are calculated as percentages:
Buyer Ratio: (buyerStrength / totalStrength) * 100
Seller Ratio: (sellerStrength / totalStrength) * 100
When buyer ratio exceeds 70%, bullish pressure dominates. When seller ratio exceeds 70%, bearish pressure dominates. The indicator also integrates ATR-based strength calculations to filter for significant moves and RSI-based strength classification (Strong/Moderate/Weak) for additional context.
3. Multi-Timeframe Alignment
The indicator requests RVOL data from three customizable timeframes (default: 5m, 15m, 60m) and calculates alignment:
mtf1_bullish = rvol_mtf1 > 1.0 and vol_mtf1 > ta.sma(vol_mtf1, 20)
mtfAlignment = (mtf1_bullish ? 1 : 0) + (mtf2_bullish ? 1 : 0) + (mtf3_bullish ? 1 : 0)
Alignment status:
Strong Aligned (3/3): All timeframes show elevated volume - high conviction
Aligned (2/3): Majority timeframes show elevated volume - moderate conviction
Weak (1/3): Only one timeframe shows elevated volume - low conviction
No Alignment (0/3): No timeframes show elevated volume - no conviction
Strong alignment across multiple timeframes indicates institutional participation at scale, as large orders are often split across timeframes to minimize market impact.
4. Flow Metrics Suite
Money Flow Index (MFI):
Volume-weighted RSI that measures buying and selling pressure:
mfi = ta.mfi(close, volumeLength)
MFI > 80 indicates overbought conditions with high volume, MFI < 20 indicates oversold conditions with high volume.
On-Balance Volume (OBV):
Cumulative volume indicator that adds volume on up days and subtracts on down days:
obv = ta.cum(math.sign(ta.change(close)) * volume)
obvTrend = obv > obvMA ? "Bullish" : obv < obvMA ? "Bearish" : "Neutral"
OBV divergences from price often signal reversals.
Accumulation/Distribution (A/D):
Measures the cumulative flow of money into and out of a security:
ad = ta.cum(close == high and close == low or high == low ? 0 : ((2 * close - low - high) / (high - low)) * volume)
Rising A/D with rising price confirms uptrend, falling A/D with rising price signals distribution.
VWAP Deviation:
Measures how far price is from volume-weighted average price:
vwap = ta.vwap(close)
vwapDeviationPercent = vwap != 0 ? ((close - vwap) / vwap) * 100 : 0
Large deviations often mean-revert as institutions take advantage of inefficient pricing.
5. Volume Speed & Acceleration
The indicator calculates volume momentum and acceleration:
Volume ROC: Rate of change in volume over 5 periods
Volume Acceleration: Change in volume ROC (second derivative)
Volume Momentum: Current volume minus 10-period SMA
Volume Trend: Increasing or Decreasing based on EMA crossover
Accelerating volume often precedes breakouts or breakdowns as institutional orders hit the market.
6. Liquidity Classification System
The indicator classifies current liquidity conditions:
Strong Buying: High RVOL + positive net pressure (buyer strength > seller strength)
Strong Selling: High RVOL + negative net pressure (seller strength > buyer strength)
Balanced: Normal RVOL with relatively equal buyer/seller strength
Thin: Low RVOL indicating reduced liquidity and potential for slippage
Pressure intensity is calculated as:
pressureLevel = math.abs(netPressure) / avgVolume
pressureIntensity = pressureLevel >= 2.0 ? "Extreme" : pressureLevel >= 1.0 ? "High" : pressureLevel >= 0.5 ? "Moderate" : "Low"
Visual Elements
RVOL Histogram: Main plot showing relative volume with color-coded intensity (extreme = magenta, high = yellow, normal = green, low = gray)
Reference Lines: Horizontal lines at 1.0 (average), 1.5 (high threshold), and 3.0 (extreme threshold)
Buyer Pressure Fill: Background fill showing buyer pressure ratio (0-100%)
Volume Oscillator: Histogram overlay showing short-term vs long-term volume momentum
MFI Line: Thick line overlay showing Money Flow Index with gradient colors
Information Table: Comprehensive dashboard displaying all metrics in real-time
The table displays 15 metrics:
1. RVOL (current relative volume)
2. Status (Extreme/High/Normal/Low/Very Low)
3. Volume Trend (Increasing/Decreasing)
4. Pressure (Bullish/Bearish/Neutral)
5. Buyer Strength (percentage)
6. Seller Strength (percentage)
7. RSI (current value)
8. Liquidity (Strong Buying/Strong Selling/Balanced/Thin)
9. MTF Alignment (Strong Aligned/Aligned/Weak/No Alignment)
10. A/D Trend (Accumulation/Distribution/Neutral)
11. OBV Trend (Bullish/Bearish/Neutral)
12. MFI (current value)
13. VWAP Deviation (percentage)
14. Volume Momentum (percentage)
Input Parameters
Volume Analysis:
Volume MA Length: Period for volume moving average (default: 20)
High RVOL Threshold: Multiplier for high volume detection (default: 1.5)
Extreme RVOL Threshold: Multiplier for extreme volume detection (default: 3.0)
Multi-Timeframe Settings:
Show Multi-Timeframe Analysis: Toggle MTF calculations (default: enabled)
Timeframe 1/2/3: Customizable timeframes for alignment analysis (default: 5m, 15m, 60m)
Buyer/Seller Strength:
ATR Length: Period for ATR calculation (default: 14)
RSI Length: Period for RSI calculation (default: 14)
RSI Overbought/Oversold: Thresholds for RSI classification (default: 70/30)
Display Options:
Show Info Table: Toggle information dashboard (default: enabled)
Show Volume Histogram: Toggle RVOL histogram (default: enabled)
Show VWAP Deviation: Toggle VWAP calculations (default: enabled)
Table Position: Choose dashboard location (Top Right/Top Left/Bottom Right/Bottom Left)
Colors:
All colors are customizable including bullish, bearish, neutral, extreme volume, and high volume colors.
How to Use This Indicator
Step 1: Monitor RVOL for Unusual Activity
Watch for RVOL spikes above 1.5 (high) or 3.0 (extreme). These indicate institutional activity. Extreme RVOL often marks climax moves or major reversals.
Step 2: Check Buyer/Seller Strength
Identify who controls the market. Buyer ratio > 70% suggests bullish control, seller ratio > 70% suggests bearish control. Look for divergences where price moves one direction but strength moves another.
Step 3: Confirm with MTF Alignment
Strong alignment across multiple timeframes confirms institutional conviction. Weak or no alignment suggests retail-driven moves that may lack follow-through.
Step 4: Analyze Flow Metrics
Check MFI, OBV, and A/D for confirmation. Rising OBV with rising price confirms uptrend. Falling A/D with rising price warns of distribution.
Step 5: Assess Liquidity Conditions
Strong Buying or Strong Selling conditions with high RVOL often precede significant moves. Thin liquidity conditions increase risk of slippage and false moves.
Step 6: Look for Volume Acceleration
Accelerating volume momentum often precedes breakouts. Decelerating volume momentum often precedes consolidation or reversal.
Best Practices
Use on liquid instruments (major forex pairs, large-cap stocks, major crypto) for most reliable signals
Combine with price action analysis - volume shows intent, price shows result
Pay attention to RVOL spikes at key support/resistance levels
Look for volume divergences: price making new highs/lows without volume confirmation often fails
MTF alignment is most reliable on trending markets, less reliable in choppy conditions
Extreme RVOL can signal exhaustion - be cautious of chasing moves with RVOL > 5.0
Use VWAP deviation for mean reversion opportunities when price extends far from VWAP
Monitor A/D and OBV for early warning signs of trend changes
Indicator Limitations
Volume analysis works best on liquid instruments with consistent volume patterns
Low-volume instruments or off-market hours can produce unreliable RVOL readings
MTF alignment requires sufficient data on all timeframes - may not work on newly listed instruments
Volume precedes price but doesn't guarantee direction - high volume can occur on both breakouts and fakeouts
Buyer/seller strength calculations assume close > open = buying and close < open = selling, which is a simplification
RVOL thresholds may need adjustment for different instruments and market conditions
The indicator shows what is happening, not why - fundamental catalysts can override technical volume patterns
Extreme RVOL can persist longer than expected during major news events or market dislocations
Technical Implementation
Built with Pine Script v6 using:
Custom RVOL calculations with dynamic thresholds
Volume-weighted buyer/seller strength analysis
Multi-timeframe security requests with proper lookahead settings
Comprehensive flow metrics (MFI, OBV, A/D, VWAP)
Volume momentum and acceleration calculations
Real-time liquidity classification system
Dynamic table with 15 metrics and color-coded cells
Thick histogram and line plots for enhanced visibility
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its comprehensive integration approach. While individual components (RVOL, MFI, OBV, A/D, buyer/seller strength) are established concepts, this indicator is justified because:
It synthesizes six distinct volume analysis methodologies into a unified system
The multi-timeframe alignment detection provides institutional conviction measurement not available in standard volume indicators
Buyer/seller strength calculations combine volume, ATR, and RSI for multi-dimensional pressure analysis
The liquidity classification system categorizes market conditions in real-time
Volume speed and acceleration metrics provide early warning of momentum shifts
The comprehensive dashboard presents 15 metrics simultaneously for holistic volume analysis
Integration of flow metrics (MFI, OBV, A/D, VWAP) with RVOL and strength analysis creates layered confirmation
Each component contributes unique information: RVOL shows intensity, buyer/seller strength shows direction, MTF alignment shows conviction, flow metrics show positioning, liquidity classification shows conditions, and volume acceleration shows momentum. The indicator's value lies in presenting these complementary perspectives simultaneously with a unified classification system.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Volume analysis is a tool for understanding market dynamics, not a crystal ball for predicting future price movement. High volume does not guarantee profitable trades. Past volume patterns do not guarantee future volume patterns. Market conditions change, and strategies that worked historically may not work in the future.
The metrics displayed are mathematical calculations based on current market data, not predictions of future price movement. High RVOL, strong buyer/seller ratios, and MTF alignment do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Thermal Momentum Gauge [JOAT]Thermal Momentum Gauge
Introduction
The Thermal Momentum Gauge is an open-source institutional-grade pressure and volatility monitoring system that combines market pressure measurement, volatility temperature analysis, volume steam detection, and multi-factor explosion identification into a unified oscillator. This sophisticated system integrates multiple proven momentum methodologies to identify high-probability explosive move conditions where pressure, temperature, and steam factors converge.
The indicator is designed for traders who understand that explosive market moves occur when multiple pressure systems align simultaneously. By synthesizing RSI pressure, WaveTrend momentum, Money Flow Index analysis, Stochastic pressure, ATR temperature, Bollinger Band width, volume steam detection, and confluence scoring, this tool helps identify structural market explosion points with thermal precision.
Why This Integration Exists
This indicator combines seven distinct pressure and volatility measurement frameworks that complement each other:
Multi-Component Pressure System: Combines RSI, WaveTrend, MFI, and Stochastic RSI for comprehensive pressure measurement
Thermal Temperature Analysis: Uses ATR and Bollinger Band width to measure market volatility temperature
Volume Steam Detection: Analyzes volume spikes and directional volume pressure for steam identification
Explosion Detection Engine: Multi-factor confluence system that identifies when all pressure systems align
Momentum Confirmation System: Ensures signals occur at genuine turning points through momentum analysis
Pressure Zone Classification: Defines thermal zones from extreme oversold to extreme overbought
Signal Filtering System: Prevents overlapping signals while maintaining precision timing
Each component addresses different aspects of market thermal dynamics. Pressure measurement reveals directional bias, temperature analysis shows volatility energy, steam detection indicates volume explosions, and confluence scoring quantifies setup quality. Together, they create a comprehensive thermal view that traditional single-dimension momentum indicators cannot provide.
Core Components Explained
1. Multi-Component Pressure System (0-100 Scale)
The system combines four pressure measurements for comprehensive analysis:
RSI Pressure:
RSI Pressure = RSI(close, rsi_length) // Standard 0-100 scale
WaveTrend Pressure:
ESA = ema(hlc3, wt_channel_length)
D = ema(abs(hlc3 - ESA), wt_channel_length)
CI = (hlc3 - ESA) / (0.015 * D)
WT1 = ema(CI, wt_average_length)
WT Pressure = (WT1 + 100) / 2 // Normalize -100 to 100 → 0 to 100
MFI Pressure:
MFI Pressure = MFI(hlc3, mfi_length) // Money Flow Index 0-100
Stochastic RSI Pressure (Optional):
Stochastic RSI = Stochastic(RSI(close, rsi_length), stoch_length)
Stoch Pressure = sma(Stochastic RSI, 3)
Combined Pressure:
Total Pressure = (RSI + WT + MFI + Stoch) / 4 // With Stochastic
Total Pressure = (RSI + WT + MFI) / 3 // Without Stochastic
2. Thermal Temperature System (0-100 Scale)
Measures market volatility energy through dual methods:
ATR-Based Temperature:
ATR Percentage = (ATR(atr_length) / close) * 100
ATR Temperature = ATR Percentage * temperature_multiplier
Bollinger Band Width Temperature (Optional):
BB Basis = sma(close, bb_length)
BB Deviation = bb_multiplier * stdev(close, bb_length)
BB Width = ((BB Upper - BB Lower) / BB Basis) * 100
BB Temperature = BB Width * 5 // Scale to 0-100
Combined Temperature:
Temperature = min((ATR Temperature + BB Temperature) / 2, 100) // With BB
Temperature = min(ATR Temperature, 100) // Without BB
3. Volume Steam Detection (0-100 Scale)
Analyzes volume explosions and directional pressure:
Volume Steam Base:
Volume Ratio = volume / sma(volume, volume_length)
Steam Base = Volume Ratio * 50
Volume Delta (Optional):
Buy Volume = close > open ? volume : 0
Sell Volume = close < open ? volume : 0
Volume Delta = (Buy Volume - Sell Volume) / volume * 50
Combined Steam:
Steam = min(Steam Base + abs(Volume Delta), 100)
Steam Classifications:
- Steam Burst: Steam > steam_threshold (default 80)
- Extreme Steam: Steam > 90
- Volume Spike Direction: Bullish (close > open) or Bearish (close < open)
4. Explosion Detection Engine
Multi-factor confluence system with momentum confirmation:
Momentum Confirmation:
Pressure Momentum = change(Total Pressure)
Pressure Acceleration = change(Pressure Momentum)
Momentum Shift = (momentum > 0 AND momentum <= 0) OR (momentum < 0 AND momentum >= 0)
Confluence Score (0-5):
Confluence Components:
- Pressure Factor: Total Pressure > pressure_threshold ? 1 : 0
- Temperature Factor: Temperature > temperature_threshold ? 1 : 0
- Steam Factor: Steam > steam_threshold ? 1 : 0
- WaveTrend Extreme: WT Pressure > 80 OR WT Pressure < 20 ? 1 : 0
- Extreme Steam: Steam > 90 ? 1 : 0
Confluence Score = Sum of all factors (0-5)
Explosion Conditions:
Explosion = Confluence Score >= minimum_confluence AND (Momentum Shift OR abs(Pressure Acceleration) > 2)
Bull Explosion = Explosion AND Total Pressure > 50 AND Pressure Momentum > 0
Bear Explosion = Explosion AND Total Pressure < 50 AND Pressure Momentum < 0
Perfect Explosion (Rare):
Perfect Explosion = Confluence Score == 5 AND abs(Pressure Momentum) > 3
Perfect Bull = Perfect Explosion AND Total Pressure > 50 AND Pressure Momentum > 0
Perfect Bear = Perfect Explosion AND Total Pressure < 50 AND Pressure Momentum < 0
5. Thermal Zone Classification
The system defines seven thermal pressure zones:
Extreme Overbought: Pressure > 80 (Critical thermal level)
Overbought: Pressure 70-80 (High thermal level)
Neutral High: Pressure 55-70 (Warm thermal level)
Equilibrium: Pressure 45-55 (Neutral thermal zone)
Neutral Low: Pressure 30-45 (Cool thermal level)
Oversold: Pressure 20-30 (Low thermal level)
Extreme Oversold: Pressure < 20 (Critical thermal level)
6. Signal Filtering System
Prevents overlapping signals while maintaining precision:
Minimum Bars Between Signals = 8
Signal Filtering Logic:
- Perfect signals take priority over regular explosions
- Regular explosions are filtered if perfect signal occurred recently
- Warning signals are filtered if explosion signals are active
- Steam bursts are filtered to minimum 3 bars apart
Visual Elements
Thermal Pressure Wave: Main oscillator with thermal gradient coloring and glow effects
Component Pressures: Individual RSI, WT, MFI, and Stochastic lines (hidden by default)
Temperature Background: Heat map style background coloring based on volatility temperature
Steam Burst Histograms: Volume spike visualization with directional coloring
Thermal Zone References: Critical levels at 20, 30, 50, 70, 80 with neutral zone highlighting
Explosion Markers: Diamond shapes for perfect explosions, triangles for regular explosions
Warning Signals: Circle markers for approaching explosion conditions
Pressure Meter: Visual gauge showing current pressure level with thermal gradient
Dashboard: Comprehensive real-time display of all thermal components and status
How Components Work Together
The integration creates a thermal momentum analysis approach:
Layer 1 - Pressure Measurement: Multi-component system reveals directional pressure across four dimensions
Layer 2 - Temperature Analysis: Volatility measurement shows market energy and expansion potential
Layer 3 - Steam Detection: Volume analysis identifies explosive energy release conditions
Layer 4 - Momentum Confirmation: Ensures signals occur at genuine turning points, not random noise
Layer 5 - Confluence Scoring: Quantifies setup quality by counting aligned factors
Layer 6 - Explosion Detection: Identifies rare moments when all thermal systems align
Layer 7 - Signal Filtering: Prevents overlap while maintaining precision timing
Example scenario: Pressure reaches extreme oversold (Layer 1) with high temperature (Layer 2), volume steam burst (Layer 3), momentum shift confirmation (Layer 4), confluence score of 5 (Layer 5), triggering perfect bull explosion (Layer 6) with proper signal filtering (Layer 7). This represents maximum thermal alignment for explosive upward move.
Input Parameters
Pressure Settings:
RSI Length: Period for RSI calculation (default: 14)
WT Channel Length: WaveTrend channel period (default: 10)
WT Average Length: WaveTrend smoothing period (default: 21)
MFI Length: Money Flow Index period (default: 14)
Stochastic Length: Stochastic RSI period (default: 14)
Use Stochastic Pressure: Toggle fourth pressure component
Temperature Settings:
ATR Length: Average True Range period (default: 14)
Temperature Multiplier: Sensitivity adjustment (default: 10.0)
Use Bollinger Band Width: Toggle BB width temperature component
BB Length: Bollinger Band period (default: 20)
BB Multiplier: Bollinger Band deviation (default: 2.0)
Volume Settings:
Volume MA Length: Volume average period (default: 20)
Steam Threshold: Volume spike multiplier (default: 2.0)
Use Volume Delta: Toggle directional volume analysis
Show Volume Spikes: Toggle volume spike visualization
Explosion Settings:
Pressure Threshold: Minimum pressure for explosion (default: 80)
Temperature Threshold: Minimum temperature for explosion (default: 70)
Steam Threshold: Minimum steam for explosion (default: 80)
Minimum Confluence Score: Required factors for explosion (default: 3)
Show Explosion Warnings: Toggle warning markers
How to Use This Indicator
Step 1: Assess Thermal Pressure
Check the main pressure gauge and current thermal zone classification in the dashboard.
Step 2: Monitor Temperature Levels
High temperature (>70) indicates market energy building for potential explosive moves.
Step 3: Watch for Steam Bursts
Volume steam bursts (>80) show explosive energy release with directional bias.
Step 4: Check Confluence Score
Scores ≥3 indicate multiple thermal factors aligning for explosion potential.
Step 5: Wait for Momentum Confirmation
Explosions require momentum shifts or acceleration to confirm genuine turning points.
Step 6: Identify Explosion Signals
Perfect explosions (diamond markers) offer highest probability, regular explosions (triangles) offer good probability.
Step 7: Monitor Warning Signals
Warning markers indicate approaching explosion conditions - prepare for potential signals.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal thermal detection
Focus on extreme thermal zones (<20 or >80) for highest explosion probability
Perfect explosions are rare but offer exceptional risk:reward opportunities
Temperature confirmation adds conviction to pressure-based signals
Steam direction (bullish/bearish) should align with expected explosion direction
Confluence scores ≥4 significantly increase explosion probability
Warning signals help prepare for upcoming explosion opportunities
Thermal zone transitions often precede significant price movements
Indicator Limitations
Thermal pressure can remain extreme longer than expected during strong trends
Perfect explosions are rare - patience required for highest probability setups
Temperature spikes during news events may create false explosion signals
Steam bursts don't guarantee immediate price movement - timing varies
Confluence scoring is mathematical, not predictive of future performance
Component pressures may conflict, requiring interpretation skills
Signal filtering may delay signals in rapidly changing market conditions
Requires understanding of multi-factor thermal analysis concepts
Technical Implementation
Built with Pine Script v6 using:
Multi-component pressure calculation with optional Stochastic RSI integration
Dual-method temperature analysis using ATR and Bollinger Band width
Advanced volume steam detection with directional bias measurement
Multi-factor confluence scoring system with momentum confirmation
Thermal gradient coloring system with glow effects and heat map backgrounds
Anti-overlap signal filtering with priority-based signal management
Real-time pressure meter visualization with thermal zone classification
Comprehensive dashboard with component breakdown and explosion status
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its thermal momentum integration approach. While individual components (RSI, WaveTrend, MFI, ATR, volume analysis) are established concepts, this integration is justified because:
It synthesizes seven distinct thermal and momentum methodologies into a unified system
The multi-component pressure system provides comprehensive momentum analysis beyond single indicators
Thermal temperature analysis combines volatility measurements for energy assessment
Volume steam detection adds explosive energy context to momentum signals
Multi-factor confluence scoring quantifies setup quality across all thermal dimensions
Perfect explosion detection identifies rare, high-probability explosive move conditions
Each component contributes unique thermal information: pressure measurement reveals directional momentum, temperature analysis shows volatility energy, steam detection indicates volume explosions, confluence scoring quantifies alignment, and momentum confirmation ensures signal quality. The integration's value lies in identifying moments when all thermal systems align simultaneously for explosive market moves.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Thermal momentum analysis and explosion detection are analytical concepts that do not guarantee future price movement. Past performance and backtested results do not guarantee future results. Market conditions change, and thermal patterns that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Prism Orderflow Detector [JOAT]Prism Orderflow Detector
Introduction
The Prism Orderflow Detector is an open-source institutional liquidity and order flow system that combines Smart Money Concepts (SMC), liquidity pool detection, Fair Value Gap analysis, Order Block identification, and advanced orderflow strength measurement into a unified overlay indicator. This comprehensive system integrates multiple proven institutional trading methodologies to identify high-probability zones where smart money positioning and retail liquidity intersect.
The indicator is designed for traders who understand that institutional players move markets by targeting liquidity pools, creating imbalances, and establishing positions through Order Blocks. By synthesizing liquidity detection, Fair Value Gaps, Order Blocks, Breaker Blocks, market structure analysis, and real-time orderflow strength measurement, this tool helps identify structural market inflection points with institutional-grade precision.
Why This Integration Exists
This indicator combines eight distinct institutional analysis frameworks that complement each other:
Liquidity Pool Detection: Identifies equal highs/lows and swing points where retail stops cluster
Order Block Analysis: Tracks institutional accumulation and distribution zones
Fair Value Gap Identification: Detects price inefficiencies created by rapid institutional moves
Breaker Block Recognition: Identifies failed Order Blocks that become new support/resistance
Market Structure Mapping: Tracks Break of Structure (BOS) and Change of Character (CHoCH)
Liquidity Heatmap Analysis: Visualizes liquidity concentration across price levels
Volume Delta Tracking: Measures real-time buying versus selling pressure
Orderflow Strength Measurement: Quantifies institutional pressure across multiple factors
Each component addresses different aspects of institutional order flow. Liquidity detection reveals where stops are hunted, Order Blocks show where institutions positioned, Fair Value Gaps indicate rapid institutional moves, market structure provides trend context, and orderflow strength quantifies current institutional pressure. Together, they create a comprehensive view of smart money activity and retail liquidity targeting.
Core Components Explained
1. Advanced Liquidity Detection System
The system identifies multiple types of liquidity pools:
Equal Highs (Buy-Side Liquidity):
Equal High Threshold = high * (threshold_percentage / 100)
Equal High Condition = (high == high ) OR (abs(high - high ) <= threshold AND high > high )
Valid Equal High = Equal High Condition AND high == highest(high, lookback_period)
Equal Lows (Sell-Side Liquidity):
Equal Low Threshold = low * (threshold_percentage / 100)
Equal Low Condition = (low == low ) OR (abs(low - low ) <= threshold AND low < low )
Valid Equal Low = Equal Low Condition AND low == lowest(low, lookback_period)
Liquidity Sweeps:
- Bullish Sweep: Price breaks below recent lows but closes back above
- Bearish Sweep: Price breaks above recent highs but closes back below
These sweeps often precede significant moves as institutions trigger retail stops before establishing positions.
2. Order Block Detection Engine
Order Blocks represent the last opposite-direction move before a strong impulse:
Bullish Order Block:
Bullish OB = close < open AND close > open AND
close > high AND (high - low ) > (ATR * strength_multiplier)
Bearish Order Block:
Bearish OB = close > open AND close < open AND
close < low AND (high - low ) > (ATR * strength_multiplier)
Order Blocks are displayed as gradient boxes with diagonal lines and extend forward to show ongoing relevance.
3. Fair Value Gap Analysis
Fair Value Gaps represent price inefficiencies where institutions moved price rapidly:
Bullish FVG:
Bullish FVG = low > high AND close > open
FVG Size = ((low - high ) / close) * 100
Valid Bullish FVG = Bullish FVG AND FVG Size >= minimum_size_percentage
Bearish FVG:
Bearish FVG = high < low AND close < open
FVG Size = ((low - high ) / close) * 100
Valid Bearish FVG = Bearish FVG AND FVG Size >= minimum_size_percentage
FVGs are displayed as horizontal lines with gradient fills and often get filled (retested) later.
4. Breaker Block System
Breaker Blocks are failed Order Blocks that become new support/resistance:
Bullish Breaker: Failed bearish Order Block that price breaks above
Bearish Breaker: Failed bullish Order Block that price breaks below
These represent significant shifts in market structure and often provide strong reversal zones.
5. Market Structure Analysis
Tracks institutional trend changes through structure breaks:
Break of Structure (BOS):
- Bullish BOS: New higher high with strong momentum
- Bearish BOS: New lower low with strong momentum
Change of Character (CHoCH):
- Bullish CHoCH: Lower low followed by higher high (trend change)
- Bearish CHoCH: Higher high followed by lower low (trend change)
6. Advanced Orderflow Features
Liquidity Heatmap:
Tracks liquidity concentration by counting touches at key levels over specified periods. High-intensity areas (>80% touch count) are highlighted as significant liquidity zones.
Volume Delta Analysis:
Buy Volume = close > open ? volume : 0
Sell Volume = close < open ? volume : 0
Volume Delta = sma(Buy Volume - Sell Volume, 14)
Volume Delta Normalized = (Volume Delta / sma(volume, 14)) * 100
Strong delta (>50) indicates institutional accumulation or distribution.
Imbalance Zone Detection:
Enhanced Fair Value Gap detection for larger inefficiencies:
Bullish Imbalance = low > high AND (low - high ) > (ATR * 0.5)
Bearish Imbalance = high < low AND (low - high) > (ATR * 0.5)
Premium/Discount Zones:
Price Range = highest(high, 50) - lowest(low, 50)
Equilibrium = lowest(low, 50) + (Price Range / 2)
Premium Zone = close > equilibrium + (Price Range * 0.25)
Discount Zone = close < equilibrium - (Price Range * 0.25)
7. Orderflow Strength Meter
Real-time quantification of institutional pressure:
Orderflow Strength = Order Block Factor + FVG Factor + Sweep Factor +
Volume Delta Factor + Structure Factor
Components:
- Order Block: ±20 points for new OBs
- FVG: ±15 points for valid FVGs
- Sweeps: ±25 points for liquidity sweeps
- Volume Delta: ±30 points (normalized)
- Structure: ±20 points for BOS/CHoCH
Strength classifications:
- Extreme Bull/Bear Pressure: >±60
- Strong Bull/Bear Pressure: >±30
Visual Elements
Liquidity Arrows: Directional arrows for equal highs/lows with clean labels
Liquidity Sweeps: Arrow lines showing sweep direction with "SWEEP" labels
Order Block Boxes: Gradient boxes with diagonal lines and "OB" labels
Fair Value Gap Lines: Horizontal lines with gradient fills and "FVG" labels
Breaker Diamonds: Diamond markers for failed Order Blocks with "BRK" labels
Structure Arrows: CHoCH arrows with directional labels
Imbalance Zones: Boxes with crossing diagonal lines and "IMB" labels
Liquidity Heatmap: Significant liquidity levels with "LIQ" labels
Volume Delta Markers: "Δ+" and "Δ-" labels for extreme volume pressure
Orderflow Background: Subtle background coloring for extreme pressure states
Dashboard: Comprehensive real-time status of all orderflow components
How Components Work Together
The integration creates a layered institutional analysis approach:
Layer 1 - Liquidity Mapping: Equal highs/lows and swing points reveal where retail stops cluster
Layer 2 - Institutional Positioning: Order Blocks show where smart money accumulated/distributed
Layer 3 - Price Inefficiencies: Fair Value Gaps indicate rapid institutional moves
Layer 4 - Structure Context: BOS/CHoCH provide trend and reversal context
Layer 5 - Failed Levels: Breaker Blocks show where previous levels failed
Layer 6 - Flow Analysis: Volume delta and heatmaps reveal current institutional pressure
Layer 7 - Strength Synthesis: Orderflow strength meter quantifies overall institutional activity
Example scenario: Price approaches equal lows (Layer 1) where a bullish Order Block exists (Layer 2), creating a Fair Value Gap on the move up (Layer 3), with bullish CHoCH confirming trend change (Layer 4), strong positive volume delta (Layer 6), and extreme bullish orderflow strength (Layer 7). This confluence suggests high-probability long opportunity.
Input Parameters
Liquidity Settings:
Show Equal Highs/Lows: Toggle liquidity pool display
Equal Price Threshold: Percentage tolerance for equal levels (default: 0.1%)
Liquidity Lookback: Period for liquidity level detection (default: 50)
Order Block Settings:
Show Order Blocks: Toggle Order Block display
Order Block Strength: ATR multiplier for OB validation (default: 3)
Extend Order Blocks: Forward extension bars (default: 20)
Fair Value Gap Settings:
Show Fair Value Gaps: Toggle FVG display
Min FVG Size: Minimum gap size percentage (default: 0.1%)
Breaker Block Settings:
Show Breaker Blocks: Toggle Breaker display
Breaker Lookback: Period for Breaker detection (default: 20)
Advanced Features:
Show Liquidity Heatmap: Toggle heatmap visualization
Show Volume Delta: Toggle volume pressure display
Show Imbalance Zones: Toggle imbalance detection
Show Premium/Discount Zones: Toggle equilibrium analysis
Show Orderflow Strength: Toggle strength background
Heatmap Period: Lookback for liquidity concentration (default: 100)
How to Use This Indicator
Step 1: Identify Market Structure
Check for recent BOS or CHoCH to understand current trend context and potential reversal zones.
Step 2: Map Liquidity Pools
Locate equal highs/lows and swing points where retail stops are likely clustered.
Step 3: Find Order Blocks
Identify recent Order Blocks where institutions likely positioned for the next move.
Step 4: Check for Fair Value Gaps
Look for unfilled FVGs that price may return to test, especially near Order Blocks.
Step 5: Monitor Liquidity Sweeps
Watch for sweep arrows indicating stop hunting - these often precede strong moves in the opposite direction.
Step 6: Analyze Volume Delta
Confirm institutional flow direction through volume delta analysis - strong delta supports directional bias.
Step 7: Review Orderflow Strength
Check dashboard for current orderflow strength - extreme readings indicate high institutional activity.
Step 8: Wait for Confluence
Best setups occur when multiple factors align: liquidity pools + Order Blocks + structure + volume confirmation.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal institutional detection
Focus on confluence zones where multiple SMC concepts align
Liquidity sweeps provide excellent risk:reward when they fail to sustain
Order Block retests often provide precise entry levels with tight stops
Fair Value Gaps act as magnets - price often returns to fill them
CHoCH signals are more significant than BOS for trend changes
Volume delta confirmation adds conviction to SMC setups
Premium/discount zones help time entries - buy discount, sell premium
Indicator Limitations
Not all liquidity pools get targeted - institutional timing varies
Order Blocks can fail if market structure changes significantly
Fair Value Gaps may never get filled during strong trending moves
Breaker Blocks don't always provide reliable support/resistance
Volume delta can be misleading in low-liquidity conditions
Orderflow strength is reactive, not predictive of future moves
SMC concepts require understanding of institutional behavior
Visual elements can clutter chart - adjust display settings as needed
Technical Implementation
Built with Pine Script v6 using:
Advanced liquidity detection with percentage-based thresholds
Real-time Order Block calculation with ATR-based validation
Dynamic Fair Value Gap identification with size filtering
Breaker Block tracking with lookback period management
Market structure analysis with BOS/CHoCH detection
Volume delta calculation with institutional bias measurement
Orderflow strength meter with multi-factor scoring
Anti-overlap filtering to prevent visual clutter
Comprehensive dashboard with real-time status updates
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its comprehensive SMC integration approach. While individual components (Order Blocks, Fair Value Gaps, liquidity detection, volume analysis) are established Smart Money Concepts, this integration is justified because:
It synthesizes eight distinct SMC methodologies into a unified system
The orderflow strength meter quantifies institutional pressure across multiple factors
Advanced liquidity heatmap visualization shows concentration levels not available elsewhere
Integrated volume delta analysis provides real-time institutional flow confirmation
Premium/discount zone analysis adds equilibrium context to SMC setups
Anti-overlap filtering and clean visual design reduce chart clutter while maintaining functionality
Each component contributes unique institutional information: liquidity detection reveals stop hunting targets, Order Blocks show positioning zones, Fair Value Gaps indicate rapid moves, market structure provides context, and volume analysis confirms flow. The integration's value lies in presenting these complementary SMC perspectives simultaneously with quantified orderflow strength measurement.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Smart Money Concepts and institutional analysis are educational frameworks that do not guarantee future price movement. Past performance and backtested results do not guarantee future results. Market conditions change, and SMC patterns that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Polarity Divergence Scanner [JOAT]Polarity Divergence Scanner
Introduction
The Polarity Divergence Scanner is an open-source advanced market polarity detection system that measures bullish and bearish pressure across four distinct market dimensions: price action, volume flow, momentum dynamics, and volatility expansion. This sophisticated oscillator integrates multiple pressure measurement techniques to detect polarity shifts, divergences, and pressure extremes with visual heatmap representation.
The indicator is designed for traders who understand that market movements are driven by the constant battle between bullish and bearish forces across multiple dimensions. By synthesizing price polarity, volume polarity, momentum polarity, and volatility polarity into a composite index, this tool helps identify structural market turning points where pressure imbalances create high-probability reversal opportunities.
Why This Integration Exists
This indicator combines four distinct polarity measurement frameworks that complement each other:
Price Polarity Analysis: Measures directional pressure from pure price action using range-normalized calculations
Volume Polarity Tracking: Analyzes buying versus selling pressure through volume flow dynamics
Momentum Polarity Detection: Combines RSI and MACD analysis to measure acceleration-based pressure
Volatility Polarity Assessment: Tracks expansion versus contraction pressure through ATR analysis
Each component addresses different aspects of market pressure dynamics. Price polarity reveals directional bias, volume polarity shows institutional flow, momentum polarity indicates acceleration changes, and volatility polarity measures market energy. Together, they create a comprehensive view of market pressure that traditional single-dimension indicators cannot provide.
Core Components Explained
1. Price Polarity Engine
Measures directional pressure from price movement:
Price Change = close - close
Price Range = highest(high, polarity_period) - lowest(low, polarity_period)
Price Polarity = (Price Change / Price Range) * 100
Smoothed Price Polarity = ema(Price Polarity, smoothing)
This calculation normalizes price movement against the recent range, providing a -100 to +100 scale where positive values indicate bullish pressure and negative values indicate bearish pressure.
2. Volume Polarity System
Analyzes buying versus selling pressure through volume flow:
Bull Volume = close > open ? volume : 0
Bear Volume = close < open ? volume : 0
Volume Polarity = ((sma(Bull Volume, period) - sma(Bear Volume, period)) / sma(Total Volume, period)) * 100
This reveals institutional flow direction by comparing accumulation (buying) volume against distribution (selling) volume over the specified period.
3. Momentum Polarity Calculator
Combines RSI and MACD for comprehensive momentum analysis:
RSI Polarity = (RSI - 50) * 2 // Scale to -100 to +100
MACD Histogram Normalized = (MACD Histogram / stdev(MACD Histogram, period)) * 30
Momentum Polarity = (RSI Polarity + MACD Normalized) / 2
This dual-momentum approach captures both relative strength (RSI) and trend acceleration (MACD) components.
4. Volatility Polarity Tracker
Measures expansion versus contraction pressure:
ATR Change = current ATR - ATR
ATR Average = sma(ATR, polarity_period)
Volatility Polarity = (ATR Change / ATR Average) * 100
Positive values indicate expanding volatility (energy building), while negative values show contracting volatility (energy dissipating).
5. Composite Polarity Index
Weighted combination of all polarity dimensions:
Composite Polarity = (Price Polarity * 0.35) + (Volume Polarity * 0.25) + (Momentum Polarity * 0.30) + (Volatility Polarity * 0.10)
Final Polarity = ema(Composite Polarity, smoothing) * sensitivity
The weighting emphasizes price and momentum while incorporating volume flow and volatility context.
6. Pressure Zone Classification
The system defines seven distinct pressure zones:
Extreme Bull Zone: Polarity ≥ 70 (Intense bullish pressure)
Strong Bull Zone: Polarity 40-69 (Solid bullish pressure)
Weak Bull Zone: Polarity 1-39 (Mild bullish pressure)
Neutral Zone: Polarity = 0 (Equilibrium state)
Weak Bear Zone: Polarity -1 to -39 (Mild bearish pressure)
Strong Bear Zone: Polarity -40 to -69 (Solid bearish pressure)
Extreme Bear Zone: Polarity ≤ -70 (Intense bearish pressure)
7. Polarity Shift Detection
The system identifies four types of polarity shifts:
Polarity Flips: Crosses zero line with sufficient strength (>30)
Polarity Acceleration: Increasing momentum in extreme zones (>50 or <-50)
Polarity Exhaustion: Weakening momentum in extreme zones (>80 or <-80)
Pressure Temperature: Volatility-adjusted intensity measurement
8. Advanced Divergence Detection
Uses pivot-based analysis to identify polarity divergences:
Bullish Polarity Divergence: Price makes lower low while polarity makes higher low
Bearish Polarity Divergence: Price makes higher high while polarity makes lower high
Divergences are filtered by minimum polarity strength to ensure significance.
Visual Elements
Composite Polarity Wave: Main oscillator with advanced gradient coloring and triple-layer glow effect
Individual Polarity Lines: Price, volume, and momentum polarity components
Polarity Strength Histogram: Background columns showing absolute polarity strength
Pressure Zone Backgrounds: Dynamic gradient backgrounds based on polarity intensity
Reference Lines: Critical levels at ±40, ±70, and ±100 with neutral zone highlighting
Polarity Shift Markers: Circles for flips, triangles for acceleration, X-crosses for exhaustion
Divergence Diamonds: Large diamond markers for confirmed polarity divergences
Dashboard: Comprehensive real-time display of all polarity components and signal status
How Components Work Together
The integration creates a multi-dimensional pressure analysis:
Layer 1 - Price Pressure: Directional bias from pure price movement
Layer 2 - Volume Pressure: Institutional flow through buying/selling volume
Layer 3 - Momentum Pressure: Acceleration and relative strength dynamics
Layer 4 - Volatility Pressure: Energy expansion/contraction context
Layer 5 - Composite Analysis: Weighted combination revealing overall market polarity
Layer 6 - Shift Detection: Identification of polarity transitions and extremes
Layer 7 - Divergence Analysis: Price-polarity disconnects signaling potential reversals
Example scenario: Price makes a new high (Layer 1) but volume polarity weakens (Layer 2), momentum polarity diverges (Layer 3), and volatility contracts (Layer 4). The composite polarity (Layer 5) shows bearish divergence (Layer 7) with exhaustion signals (Layer 6), indicating high reversal probability.
Input Parameters
Polarity Core:
Polarity Period: Base period for polarity calculations (default: 14)
Smoothing: Smoothing factor for polarity waves (default: 3)
Sensitivity: Signal sensitivity multiplier (default: 1.5)
Divergence Detection:
Pivot Lookback: Bars for pivot detection (default: 5)
Min Divergence Strength: Minimum polarity strength for signals (default: 60)
Visual Settings:
Show Polarity Waves: Toggle main polarity display
Show Pressure Zones: Toggle background zone coloring
Show Divergence Markers: Toggle divergence signals
Show Polarity Shifts: Toggle shift detection markers
How to Use This Indicator
Step 1: Assess Overall Polarity
Check the composite polarity level and current pressure zone classification in the dashboard.
Step 2: Identify Pressure Extremes
Look for extreme bull (>70) or extreme bear (<-70) zones where reversals are more likely.
Step 3: Monitor Polarity Shifts
Watch for polarity flips (zero line crosses), acceleration in extreme zones, or exhaustion signals.
Step 4: Analyze Component Divergences
Check if individual polarity components (price, volume, momentum) are aligned or diverging.
Step 5: Detect Polarity Divergences
Look for diamond markers indicating price-polarity divergences - these often precede major reversals.
Step 6: Confirm with Pressure Temperature
High pressure temperature (volatility-adjusted intensity) adds conviction to polarity signals.
Step 7: Wait for Confluence
Best setups occur when multiple factors align: extreme zones + polarity shifts + divergences + high temperature.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal polarity detection
Focus on extreme zones (>70 or <-70) for highest probability reversals
Polarity flips provide early trend change signals with good risk:reward
Divergences in extreme zones offer exceptional reversal opportunities
Acceleration signals in extreme zones often precede explosive moves
Exhaustion signals warn of potential polarity reversals before they occur
High pressure temperature adds conviction to all polarity signals
Component analysis helps understand the source of polarity changes
Indicator Limitations
Polarity can remain extreme longer than expected during strong trends
Divergences may take time to resolve - patience is required
Extreme zones don't guarantee immediate reversals - timing is crucial
Component polarity may conflict, requiring interpretation skills
Pressure temperature can spike during news events, creating false signals
Polarity shifts may be brief and require quick decision-making
Performance varies across different market conditions and volatility regimes
Requires understanding of multi-dimensional pressure analysis concepts
Technical Implementation
Built with Pine Script v6 using:
Multi-dimensional polarity calculation across four market aspects
Advanced gradient coloring system with triple-layer glow effects
Pivot-based divergence detection with strength filtering
Dynamic pressure zone classification with background visualization
Real-time polarity shift detection with multiple signal types
Comprehensive dashboard with component breakdown and signal status
Anti-overlap filtering to prevent signal clustering
Pressure temperature calculation for volatility-adjusted intensity
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its multi-dimensional polarity approach. While individual components (RSI, MACD, volume analysis, ATR) are established concepts, this integration is justified because:
It synthesizes four distinct polarity dimensions that address different market pressure aspects
The composite polarity index provides a unified view of market pressure across multiple dimensions
Advanced polarity shift detection identifies transitions before they become obvious in price
Polarity divergence analysis reveals price-pressure disconnects that traditional indicators miss
Pressure zone classification provides quantitative framework for market state assessment
Pressure temperature adds volatility context to polarity intensity measurements
Each component contributes unique polarity information: price polarity shows directional bias, volume polarity reveals institutional flow, momentum polarity indicates acceleration, and volatility polarity measures energy. The integration's value lies in identifying moments when these pressure dimensions align or diverge, creating high-probability trading opportunities.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Polarity analysis and divergence detection are analytical concepts that do not guarantee future price movement. Past performance and backtested results do not guarantee future results. Market conditions change, and polarity patterns that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Harmonic Pulse Tracker [JOAT]Harmonic Pulse Tracker
Introduction
The Harmonic Pulse Tracker is an open-source institutional-grade wave and rhythm analysis system that combines Elliott Wave principles, Fibonacci harmonic analysis, WaveTrend oscillator mechanics, and cycle detection into a unified oscillator. This sophisticated system integrates multiple proven methodologies to identify high-probability reversal zones where harmonic patterns, wave cycles, and momentum indicators converge.
The indicator is designed for traders who understand that market movements follow natural harmonic patterns and cyclical rhythms. By synthesizing detrended price oscillation, Fibonacci retracement levels, WaveTrend momentum analysis, money flow dynamics, and volume confirmation, this tool helps identify structural market turning points with mathematical precision.
Why This Integration Exists
This indicator combines six distinct analytical frameworks that complement each other:
Harmonic Wave Analysis: Uses detrended price oscillation combined with Ehlers cycle detection to identify natural market rhythms
Fibonacci Harmonic Levels: Calculates dynamic Fibonacci retracements and extensions based on wave swing points
WaveTrend Oscillator: Implements LazyBear's WaveTrend algorithm for momentum and overbought/oversold detection
Money Flow Integration: Tracks institutional buying and selling pressure through Money Flow Index analysis
Volume Analysis: Confirms wave movements with volume spikes and directional volume pressure
Elliott Wave Counting: Simplified wave counting system to identify impulse and corrective wave phases
Each component addresses different aspects of market rhythm and harmony. The harmonic wave engine identifies natural price cycles, Fibonacci levels provide mathematical support/resistance, WaveTrend shows momentum extremes, money flow reveals institutional activity, volume confirms genuine moves, and Elliott Wave counting provides structural context. Together, they create a multi-dimensional view of market harmony and discord.
Core Components Explained
1. Harmonic Wave Engine
The core wave calculation combines two advanced techniques:
DPO (Detrended Price Oscillator) = close - sma(close, length/2 + 1)
Ehlers Cycle Component = High-pass filtered price with cycle smoothing
Harmonic Wave = Smoothed DPO + (Cycle Component * 0.5)
This creates a wave that removes trend bias while preserving cyclical components, revealing the natural harmonic rhythm of price movement.
Wave Derivatives:
- Wave Momentum: Rate of change in harmonic wave
- Wave Acceleration: Rate of change in momentum
- Wave Velocity: Percentage rate of change over 5 periods
These derivatives help identify wave phase transitions and momentum shifts before they become obvious in price.
2. Fibonacci Harmonic Level System
The indicator calculates dynamic Fibonacci levels based on harmonic wave swing points:
Standard Retracements:
- 23.6%, 38.2%, 50.0%, 61.8%, 78.6% of wave range
Extensions:
- 127.2%, 161.8%, 261.8% beyond wave high
Golden Pocket Zone:
The critical 61.8% to 78.6% retracement zone where most harmonic reversals occur. This zone represents the mathematical sweet spot where Fibonacci ratios converge with natural market rhythm.
Harmonic Resonance Detection:
The system identifies when price is within 5% of key Fibonacci levels and calculates confluence scores when multiple levels align.
3. WaveTrend Oscillator Integration
Implements the proven WaveTrend algorithm:
ESA = ema(hlc3, channel_length)
D = ema(abs(hlc3 - ESA), channel_length)
CI = (hlc3 - ESA) / (0.015 * D)
WT1 = ema(CI, average_length)
WT2 = sma(WT1, 4)
WaveTrend Signals:
- Crossovers in oversold zone (< -50): Bullish reversal signals
- Crossunders in overbought zone (> 50): Bearish reversal signals
- Regular crossovers: Momentum shift confirmation
4. Money Flow Analysis
Tracks institutional buying and selling pressure:
MFI = Money Flow Index over specified period
MFI Centered = (MFI - 50) * multiplier
- Positive MFI: Institutional buying pressure
- Negative MFI: Institutional selling pressure
- Strong MFI: Absolute value > 25 indicates significant institutional activity
5. Volume Analysis Engine
Comprehensive volume analysis including:
Volume Spikes: Volume > Average Volume * Threshold
Volume Ratio: Current volume / Average volume
Volume Strength: Normalized volume intensity (0-100)
Directional Volume:
- Bullish Volume Spike: High volume + green candle
- Bearish Volume Spike: High volume + red candle
6. Elliott Wave Phase Detection
Simplified wave analysis to identify market structure:
Impulse Waves:
- Impulse Up: Positive momentum + acceleration + velocity
- Impulse Down: Negative momentum + acceleration + velocity
Corrective Waves:
- Mixed momentum and acceleration signals indicating consolidation
Wave Counting:
Basic 5-wave count system that resets after wave 5 completion, helping identify potential reversal zones.
Multi-Factor Confluence Scoring System
The indicator calculates a real-time confluence score (0-100) by weighting each component:
Confluence Score Components:
- Fibonacci Zone: Up to 20 points (Golden Pocket = 20, other Fib levels = 4 each)
- Wave Strength: Up to 20 points (based on wave momentum intensity)
- WaveTrend: Up to 20 points (extreme zone crossovers = 20, regular = 15)
- Money Flow: Up to 20 points (strong institutional activity = 20)
- Volume: Up to 20 points (volume spikes = 20, elevated = 15)
Scores above 80 indicate exceptional confluence for potential trades. The dashboard displays individual component scores for transparency.
Perfect Harmonic Alignment Detection
The system identifies rare "Perfect Harmonic" setups when:
- Price is in Golden Pocket zone
- Impulse wave phase is active
- Wave strength > 70
- WaveTrend crossover in extreme zone
- Positive money flow (for bullish) or negative (for bearish)
- Volume spike confirmation
These setups represent the highest probability reversal opportunities.
Visual Elements
Harmonic Wave: Main oscillator with gradient coloring based on wave position
Wave Momentum: Histogram showing rate of change in wave movement
Fibonacci Levels: Key retracement and extension levels (38.2%, 50%, 61.8%, 78.6%, 161.8%)
Golden Pocket Zone: Highlighted area between 61.8% and 78.6% levels
WaveTrend Lines: WT1 and WT2 with overbought/oversold zones
Money Flow Columns: Institutional buying/selling pressure visualization
Volume Strength: Volume intensity histogram
Signal Markers: Perfect Harmonic signals and strong confluence alerts
Background Zones: Golden Pocket and Perfect Signal highlighting
Dashboard: Real-time display of all component values and confluence score
How Components Work Together
The integration creates a harmonic analysis approach:
Layer 1 - Wave Rhythm: Harmonic wave identifies natural market cycles and turning points
Layer 2 - Mathematical Levels: Fibonacci ratios provide precise support/resistance zones
Layer 3 - Momentum Context: WaveTrend shows overbought/oversold extremes
Layer 4 - Institutional Flow: Money flow reveals smart money positioning
Layer 5 - Volume Confirmation: Volume analysis validates genuine moves vs noise
Layer 6 - Wave Structure: Elliott Wave context provides structural framework
Example scenario: Harmonic wave reaches Golden Pocket zone (Layer 1 + 2) during WaveTrend oversold crossover (Layer 3) with positive money flow (Layer 4) and volume spike (Layer 5) in corrective wave phase (Layer 6). This confluence suggests exceptional reversal probability.
Input Parameters
Wave Settings:
Wave Length: Period for harmonic wave calculation (default: 34)
Smoothing Period: Wave smoothing factor (default: 5)
WaveTrend Settings:
Show WaveTrend: Toggle WaveTrend display
WT Channel Length: Channel calculation period (default: 9)
WT Average Length: Smoothing period (default: 12)
WT Overbought: Overbought threshold (default: 50)
WT Oversold: Oversold threshold (default: -50)
Money Flow Settings:
Show Money Flow: Toggle money flow display
MFI Length: Money Flow Index period (default: 14)
MFI Multiplier: Sensitivity adjustment (default: 1.5)
Volume Settings:
Show Volume Analysis: Toggle volume indicators
Volume Spike Threshold: Multiplier for spike detection (default: 1.5)
Fibonacci Settings:
Show Fibonacci Levels: Toggle Fibonacci level display
Fibonacci Lookback: Period for swing point calculation (default: 100)
Cycle Settings:
Cycle Period: Ehlers cycle detection period (default: 20)
Cycle Smoothing: Cycle component smoothing (default: 3)
How to Use This Indicator
Step 1: Identify Wave Phase
Check the dashboard for current wave phase (Impulse Up/Down, Corrective, Neutral) and Elliott Wave count.
Step 2: Locate Fibonacci Zones
Look for price approaching key Fibonacci levels, especially the Golden Pocket zone (61.8%-78.6%).
Step 3: Check WaveTrend Position
Identify if WaveTrend is in extreme zones and watch for crossovers in oversold/overbought areas.
Step 4: Analyze Money Flow
Confirm institutional positioning through Money Flow Index - positive for bullish setups, negative for bearish.
Step 5: Verify Volume Confirmation
Ensure volume supports the move - look for volume spikes in the direction of the expected reversal.
Step 6: Review Confluence Score
Check the dashboard confluence score. Scores above 80 indicate high-probability setups.
Step 7: Wait for Perfect Harmonic Signals
The highest probability trades occur when "PERFECT" signals appear, indicating all factors are aligned.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal harmonic detection
Focus on Golden Pocket zone entries - this is where most harmonic reversals occur
Wait for WaveTrend crossovers in extreme zones for best risk:reward
Confirm with money flow direction - institutional flow should support the trade direction
Volume spikes add significant confirmation to harmonic setups
Perfect Harmonic signals are rare but offer exceptional probability
Wave 5 completions often coincide with major reversal opportunities
Use confluence scores above 80 as primary filter for trade selection
Indicator Limitations
Harmonic patterns can extend beyond expected Fibonacci levels
Perfect Harmonic signals are rare - patience is required for best setups
Wave counting is simplified and may not match complex Elliott Wave analysis
Fibonacci levels are dynamic and may adjust as new swing points form
Money flow can remain extreme longer than expected during strong trends
Volume confirmation may be less reliable in low-liquidity markets
Confluence scoring is mathematical, not predictive of future performance
Requires understanding of harmonic analysis principles for effective use
Technical Implementation
Built with Pine Script v6 using:
Advanced detrended price oscillation with Ehlers cycle detection
Dynamic Fibonacci calculation based on swing point analysis
LazyBear WaveTrend algorithm implementation
Real-time Money Flow Index with institutional bias detection
Volume analysis with spike detection and directional confirmation
Simplified Elliott Wave counting with phase detection
Multi-factor confluence scoring system with component weighting
Anti-overlap signal filtering to prevent signal clustering
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its harmonic integration approach. While individual components (DPO, Fibonacci, WaveTrend, MFI, volume analysis, Elliott Wave) are established concepts, this integration is justified because:
It synthesizes six distinct methodologies that address different aspects of market harmony
The harmonic wave engine combines detrended oscillation with cycle detection for superior rhythm analysis
Dynamic Fibonacci levels adjust to current wave structure rather than using static retracements
Golden Pocket zone identification provides mathematical precision for reversal timing
Multi-factor confluence scoring quantifies setup quality across all components
Perfect Harmonic detection identifies rare, high-probability reversal opportunities
Each component contributes unique harmonic information: wave analysis reveals natural cycles, Fibonacci provides mathematical levels, WaveTrend shows momentum extremes, money flow indicates institutional positioning, volume confirms genuine moves, and Elliott Wave provides structural context. The integration's value lies in identifying moments when all these harmonic factors align simultaneously.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Harmonic analysis and Fibonacci levels are mathematical concepts that do not guarantee future price movement. Past performance and backtested results do not guarantee future results. Market conditions change, and harmonic patterns that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Cascade Trend Navigator [JOAT]Cascade Trend Navigator
Introduction
The Cascade Trend Navigator is an open-source institutional-grade multi-timeframe trend and flow system that combines dynamic support/resistance zones, volume profile analysis, and liquidity detection into a unified overlay indicator. This comprehensive system integrates multiple proven methodologies to identify high-probability trend continuation and reversal zones where institutional and retail liquidity converge.
The indicator is designed for traders who understand that successful trend following requires more than simple moving average crossovers. By synthesizing adaptive moving averages, dynamic support/resistance zones, volume profile analysis, and liquidity pool detection, this tool helps identify structural market inflection points with institutional-grade precision.
Why This Integration Exists
This indicator combines four distinct analytical frameworks that complement each other:
Adaptive Moving Average System: Uses Hull, TEMA, DEMA, ZEMA, and VWMA calculations for superior trend identification with reduced lag
Dynamic Support/Resistance Zones: Calculates real-time zones using Hull Moving Averages and ATR-based deviation bands
Volume Profile Analysis: Identifies Point of Control (POC) and high-volume price levels where institutional activity concentrates
Liquidity Pool Detection: Tracks equal highs/lows, swing points, and liquidity zones where stop hunts typically occur
Each component addresses different aspects of market structure. The adaptive MA system provides trend direction with minimal lag, dynamic zones reveal real-time support/resistance levels, volume profile shows where institutions are most active, and liquidity detection identifies areas where price reversals are likely. Together, they create a multi-dimensional view of market flow and structure.
Core Components Explained
1. Advanced Moving Average Engine
The indicator offers seven different moving average types, each optimized for specific market conditions:
Hull MA (HMA): wma(2 * wma(src, length/2) - wma(src, length), sqrt(length))
TEMA: 3 * ema1 - 3 * ema2 + ema3 (Triple smoothed)
DEMA: 2 * ema1 - ema2 (Double smoothed)
ZEMA: Zero-lag EMA with lag compensation
VWMA: Volume-weighted for institutional flow tracking
The system uses three MA periods: Fast (default 20), Slow (default 50), and Trend (default 200). Trend direction is determined when Fast MA > Slow MA and price > Trend MA for bullish conditions, with the inverse for bearish conditions.
2. Dynamic Support/Resistance Zone System
Unlike static pivot levels, these zones adapt to current market volatility:
Resistance Zone: HMA(high, length) + (ATR * deviation) to HMA(high, length)
Support Zone: HMA(low, length) to HMA(low, length) - (ATR * deviation)
The zones automatically adjust width based on ATR, making them more relevant during high volatility periods and tighter during consolidation. This adaptive nature provides more accurate entry and exit levels compared to fixed percentage-based zones.
3. Volume Profile Integration
The indicator calculates a real-time volume profile over a specified lookback period:
- Divides the price range into configurable bins (default 20)
- Accumulates volume for each price level
- Identifies Point of Control (POC) - the price level with highest volume
- Displays POC as a dynamic level where institutional activity is concentrated
This helps traders understand where the majority of trading activity occurred and where price is likely to find support or resistance based on volume acceptance.
4. Liquidity Pool Detection System
The system identifies multiple types of liquidity pools:
Equal Highs/Lows: Price levels where multiple highs or lows form at similar levels, creating liquidity pools for institutional players to target
Swing Points: Pivot highs and lows that represent areas where retail stops are likely clustered
Liquidity Sweeps: Instances where price briefly moves beyond recent highs/lows but fails to sustain, indicating stop hunting activity
These areas often precede significant price moves as institutions clear retail positions before establishing their own.
5. Trend Strength Calculation
The indicator calculates trend strength as:
Trend Strength = abs((Fast MA - Slow MA) / Slow MA) * 100
This provides a quantitative measure of trend momentum, helping traders distinguish between strong trending moves and weak corrective phases.
Visual Elements
Moving Average Cloud: Fill between Fast and Slow MAs with gradient coloring based on trend direction
Dynamic Zones: Support zones in green, resistance zones in red with glowing borders
POC Line: Golden cross marking the highest volume price level
Liquidity Markers: Triangles for equal highs/lows, diamonds for swing points
Signal Arrows: BUY/SELL labels for trend changes and zone touches
Trend Background: Subtle background coloring indicating overall market bias
Dashboard: Real-time display of trend status, strength, and distances to key levels
How Components Work Together
The integration creates a layered analysis approach:
Layer 1 - Trend Identification: Adaptive MAs determine primary trend direction with minimal lag
Layer 2 - Dynamic Levels: Support/resistance zones provide entry and exit levels that adapt to volatility
Layer 3 - Volume Confirmation: POC shows where institutions are most active
Layer 4 - Liquidity Mapping: Equal highs/lows and swing points reveal where reversals are likely
Layer 5 - Signal Synthesis: All components combine to generate high-probability trade signals
Example scenario: Price approaches a dynamic support zone (Layer 2) in an uptrend (Layer 1), near the POC level (Layer 3), with equal lows nearby (Layer 4). This confluence suggests a high-probability bounce location.
Input Parameters
Trend Settings:
Fast MA Length: Period for fast moving average (default: 20)
Slow MA Length: Period for slow moving average (default: 50)
Trend MA Length: Period for trend filter (default: 200)
MA Type: Choose from SMA, EMA, HMA, TEMA, DEMA, ZEMA, VWMA
Show MA Cloud: Toggle cloud fill between fast and slow MAs
Zone Settings:
Zone Calculation Length: Period for HMA zone calculation (default: 50)
Zone Deviation: ATR multiplier for zone width (default: 1.5)
Show Support/Resistance Zones: Toggle zone display
Volume Profile Settings:
Volume Profile Length: Lookback period for volume calculation (default: 100)
Number of Price Bins: Granularity of volume profile (default: 20)
Show Volume Profile: Toggle POC display
Liquidity Settings:
Show Liquidity Zones: Toggle liquidity markers
Liquidity Lookback: Period for swing point detection (default: 50)
How to Use This Indicator
Step 1: Identify Trend Direction
Check the MA cloud color and trend background. Green indicates bullish trend, red indicates bearish trend.
Step 2: Locate Dynamic Zones
Identify current support and resistance zones. These adapt to volatility and provide better levels than static pivots.
Step 3: Check Volume Profile
Note the POC level - this shows where most institutional activity occurred and often acts as magnetic price level.
Step 4: Map Liquidity Pools
Look for equal highs/lows and swing points. These areas often see stop hunting before major moves.
Step 5: Wait for Confluence
Best setups occur when multiple elements align: trend direction + zone touch + POC proximity + liquidity pool.
Step 6: Monitor Dashboard
Use the dashboard to track trend strength, distances to key levels, and current signal status.
Best Practices
Use on 15-minute to daily timeframes for optimal signal quality
Combine with proper risk management - zones provide levels, not exact entries
Pay attention to trend strength - stronger trends have higher continuation probability
Watch for zone touches in trending markets as continuation signals
Liquidity sweeps often provide excellent risk:reward entries when they fail
POC acts as magnetic level - price often returns to test these areas
Volume confirmation is critical - avoid signals during low volume periods
Indicator Limitations
Does not provide exact entry/exit signals - requires trader interpretation
Can generate false signals in choppy, sideways markets
Dynamic zones may adjust too quickly in highly volatile conditions
Volume profile requires sufficient lookback data to be meaningful
Liquidity pools don't always get tested - not every level provides opportunity
Trend strength can remain elevated longer than expected during strong moves
Performance varies across different markets and timeframes
Requires understanding of institutional order flow concepts for effective use
Technical Implementation
Built with Pine Script v6 using:
Advanced moving average calculations with zero-lag techniques
Real-time volume profile computation with dynamic binning
Adaptive support/resistance zone calculation using HMA and ATR
Pivot-based liquidity pool detection with swing analysis
Dynamic color gradients based on trend strength and direction
Comprehensive dashboard with real-time statistics
Anti-overlap signal filtering to prevent signal clustering
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its integration approach. While individual components (moving averages, support/resistance, volume profile, liquidity detection) are established concepts, this integration is justified because:
It synthesizes four distinct methodologies that address different market aspects
The adaptive zone calculation provides dynamic levels that adjust to current volatility
Volume profile integration shows institutional activity concentration in real-time
Liquidity pool detection reveals areas where institutional stop hunting typically occurs
The combination helps identify confluence zones where multiple factors align
Anti-overlap filtering and trend strength calculation provide quantitative edge
Each component contributes unique information: adaptive MAs provide trend direction with minimal lag, dynamic zones offer volatility-adjusted levels, volume profile reveals institutional activity, and liquidity detection identifies reversal zones. The integration's value lies in presenting these complementary perspectives simultaneously with unified signal generation.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Technical indicators are tools for analysis, not guarantees of future performance. Past performance and backtested results do not guarantee future results. Market conditions change, and strategies that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Adaptive Hull Momentum Ribbon [JOAT]Adaptive Hull Momentum Ribbon
Introduction
The Adaptive Hull Momentum Ribbon is an open-source trend-following indicator that combines a 5-layer Hull Moving Average (HMA) ribbon with EMA cloud analysis, key moving averages (SMA 50/200, EMA 200), crossover detection, and comprehensive trend strength analytics. This mashup creates a multi-layered trend identification system designed to show not just trend direction, but trend quality, alignment across multiple timeframes, and confluence between different moving average methodologies.
The indicator addresses a fundamental challenge in trend trading: single moving averages provide limited information about trend strength and quality. By layering five HMAs with different periods, adding an EMA cloud for short-term momentum, and tracking alignment with key institutional moving averages, this tool provides a complete picture of trend health that helps traders distinguish between strong trends worth following and weak trends likely to fail.
Chart showing 5-layer HMA ribbon, EMA cloud, and key MAs with trend dashboard on D timeframe
Why This Mashup Exists
This indicator combines four moving average frameworks that complement each other:
Hull Moving Average Ribbon: 5 HMAs (8, 13, 21, 34, 55) providing smooth, responsive trend indication
EMA Cloud: Fast (9) and Slow (21) EMAs showing short-term momentum
Key Institutional MAs: SMA 50, SMA 200, EMA 200 tracked by institutions globally
Crossover Detection: Golden Cross, Death Cross, and HMA crossovers
Each component serves a specific purpose: HMA Ribbon shows trend with minimal lag, EMA Cloud captures short-term momentum shifts, Key MAs provide institutional reference levels, and Crossovers signal major trend changes. Together, they create a comprehensive trend analysis system that shows both micro (HMA/EMA) and macro (SMA 50/200) trend structure.
The mashup is justified because these moving average types use fundamentally different calculations (weighted moving average with square root period for HMA, exponential weighting for EMA, simple average for SMA) that respond to price changes differently. When they align, it indicates genuine trend strength across multiple calculation methods and timeframes.
Core Components Explained
1. Hull Moving Average Ribbon System
HMA calculation provides smooth, responsive moving averages with reduced lag:
// Hull Moving Average formula
hullMA(src, length) =>
wma1 = ta.wma(src, length / 2)
wma2 = ta.wma(src, length)
ta.wma(2 * wma1 - wma2, int(math.sqrt(length)))
// 5-layer ribbon
hma8 = hullMA(close, 8) // Fastest, most responsive
hma13 = hullMA(close, 13)
hma21 = hullMA(close, 21) // Medium-term trend
hma34 = hullMA(close, 34)
hma55 = hullMA(close, 55) // Slowest, smoothest
HMA advantages over traditional MAs:
Significantly reduced lag compared to SMA/EMA
Smooth line without excessive whipsaws
Responsive to price changes while filtering noise
Square root period weighting provides optimal balance
Ribbon interpretation:
Full Bullish Alignment: HMA8 > HMA13 > HMA21 > HMA34 > HMA55 = strong uptrend
Full Bearish Alignment: HMA8 < HMA13 < HMA21 < HMA34 < HMA55 = strong downtrend
Mixed Alignment: HMAs crossing or intertwined = weak trend or consolidation
Ribbon Width: Wide ribbon = strong trend, narrow ribbon = weak trend
The indicator plots all 5 HMAs with gradient coloring (green to red) and fills between them to create visual ribbon effect.
2. EMA Cloud System
Fast and slow EMAs create a cloud showing short-term momentum:
emaFast = ta.ema(close, 9) // Short-term momentum
emaSlow = ta.ema(close, 21) // Medium-term trend
// Cloud color
emaCloudBullish = emaFast > emaSlow
emaCloudBearish = emaFast < emaSlow
EMA Cloud significance:
Fast EMA above Slow EMA = bullish momentum
Fast EMA below Slow EMA = bearish momentum
Cloud acts as dynamic support/resistance
Cloud thickness indicates momentum strength
Price above cloud = bullish, below cloud = bearish
The indicator fills the area between fast and slow EMAs with color based on direction (green for bullish, red for bearish).
3. Key Institutional Moving Averages
Three widely-watched institutional moving averages:
sma50 = ta.sma(close, 50) // Short-term institutional trend
sma200 = ta.sma(close, 200) // Long-term institutional trend
ema200 = ta.ema(close, 200) // Alternative long-term trend
// Golden Cross / Death Cross
goldenCross = sma50 > sma200 // Bullish long-term
deathCross = sma50 < sma200 // Bearish long-term
Key MA significance:
SMA 50: Short-term institutional trend, strong support/resistance
SMA 200: Most watched long-term trend indicator globally
EMA 200: More responsive alternative to SMA 200
Golden Cross: SMA 50 crosses above SMA 200 = major bullish signal
Death Cross: SMA 50 crosses below SMA 200 = major bearish signal
These MAs are plotted with distinct colors and act as major support/resistance levels.
4. Comprehensive Crossover Detection
The indicator detects multiple types of crossovers:
// Golden Cross / Death Cross (major signals)
goldenCross = ta.crossover(sma50, sma200)
deathCross = ta.crossunder(sma50, sma200)
// EMA Cloud crossovers (momentum shifts)
emaBullCross = ta.crossover(emaFast, emaSlow)
emaBearCross = ta.crossunder(emaFast, emaSlow)
// HMA fast crossovers (early trend changes)
hmaFastBullCross = ta.crossover(hma8, hma13)
hmaFastBearCross = ta.crossunder(hma8, hma13)
Crossover hierarchy:
Golden/Death Cross: Major long-term trend changes (rare, very significant)
EMA Crossovers: Medium-term momentum shifts (moderate frequency)
HMA Crossovers: Short-term trend changes (frequent, early signals)
The indicator marks crossovers with shapes: circles for Golden/Death Cross, triangles for EMA crossovers, diamonds for HMA crossovers.
5. Trend Strength Analytics
Comprehensive trend strength calculation:
// Calculate alignment score
alignmentScore = 0
alignmentScore := (close > hma8 ? 1 : -1) +
(close > hma13 ? 1 : -1) +
(close > hma21 ? 1 : -1) +
(close > hma34 ? 1 : -1) +
(close > hma55 ? 1 : -1) +
(close > emaFast ? 1 : -1) +
(close > emaSlow ? 1 : -1) +
(close > sma50 ? 1 : -1) +
(close > sma200 ? 1 : -1)
// Normalize to 0-100 scale
trendStrength = (alignmentScore + 9) / 18 * 100
Trend Strength interpretation:
75-100: STRONG BULL - price above all MAs, high-quality uptrend
55-74: BULL - price above most MAs, moderate uptrend
45-54: NEUTRAL - mixed signals, no clear trend
26-44: BEAR - price below most MAs, moderate downtrend
0-25: STRONG BEAR - price below all MAs, high-quality downtrend
Example showing full HMA alignment with 55% trend strength score
Confluence Scoring System
The indicator calculates a confluence score showing agreement between different MA systems:
Confluence Score Components:
- HMA Trend: +3 if full alignment, 0 if mixed, -3 if opposite
- EMA Cloud: +2 if bullish, -2 if bearish
- Price vs SMA 50: +1 if above, -1 if below
- Price vs SMA 200: +2 if above, -2 if below
- SMA 50 vs 200: +2 if golden cross, -2 if death cross
Total Range: -10 to +10
Confluence interpretation:
+8 to +10: STRONG confluence - all systems aligned bullish
+5 to +7: MODERATE confluence - most systems bullish
-4 to +4: WEAK confluence - mixed or conflicting signals
-7 to -5: MODERATE confluence - most systems bearish
-10 to -8: STRONG confluence - all systems aligned bearish
Enhanced Dashboard System
The dashboard (top-right position) displays 9 rows:
Row 1: MA System header
Row 2: Trend classification (STRONG BULL/BULL/NEUTRAL/BEAR/STRONG BEAR)
Row 3: Trend Strength percentage (0-100%)
Row 4: HMA Alignment status (Bullish/Bearish/Mixed)
Row 5: EMA Cloud status (Bullish/Bearish)
Row 6: Price vs 200 MA (Above/Below)
Row 7: 50 vs 200 MA (Golden/Death)
Row 8: Confluence score (-10 to +10)
Row 9: Confluence strength (STRONG/MODERATE/WEAK)
Dashboard showing trend metrics with color-coded confluence score
Visual Elements
HMA Ribbon: 5 HMA lines with gradient coloring (green to red) and fills between lines
EMA Cloud: Filled area between fast and slow EMAs with transparency
SMA 50: Blue line (short-term institutional trend)
SMA 200: Orange line (long-term institutional trend)
EMA 200: Purple line (alternative long-term trend)
Golden/Death Cross Markers: Large circles at major crossovers
EMA Cross Markers: Small triangles at EMA crossovers
HMA Cross Markers: Tiny diamonds at HMA crossovers
Dashboard: Comprehensive table with all trend metrics
How Components Work Together
The mashup creates layered trend analysis:
Layer 1 - Micro Trend: HMA 8/13 crossovers show earliest trend changes
Layer 2 - Short-Term Momentum: EMA cloud shows momentum direction
Layer 3 - Medium-Term Trend: HMA 21/34/55 ribbon shows established trend
Layer 4 - Institutional Trend: SMA 50/200 show long-term institutional bias
Layer 5 - Synthesis: Trend strength and confluence scores combine all layers
Example scenario: HMA 8 crosses above HMA 13 (Layer 1), EMA cloud turns bullish (Layer 2), all 5 HMAs align bullish (Layer 3), price is above SMA 50 and SMA 200 in golden cross (Layer 4). Trend strength reaches 92% and confluence score is +9 (Layer 5), signaling extremely strong uptrend with all systems aligned.
Input Parameters
HMA Ribbon Settings:
Show HMA Ribbon: Toggle ribbon display (default: enabled)
HMA 1 Length: Fastest HMA (default: 8)
HMA 2 Length: (default: 13)
HMA 3 Length: (default: 21)
HMA 4 Length: (default: 34)
HMA 5 Length: Slowest HMA (default: 55)
EMA Cloud Settings:
Show EMA Cloud: Toggle cloud display (default: enabled)
Fast EMA: Short-term EMA (default: 9)
Slow EMA: Medium-term EMA (default: 21)
Cloud Transparency: Adjust fill transparency (default: 85)
Key MA Settings:
Show SMA 50: Toggle SMA 50 (default: enabled)
Show SMA 200: Toggle SMA 200 (default: enabled)
Show EMA 200: Toggle EMA 200 (default: enabled)
Crossover Settings:
Show Crossovers: Toggle crossover markers (default: enabled)
Show Golden/Death Cross: Major crossovers (default: enabled)
Show EMA Crossovers: EMA cloud crossovers (default: enabled)
Show HMA Crossovers: HMA fast crossovers (default: enabled)
Display Options:
Show Trend Strength: Toggle dashboard (default: enabled)
Ribbon Transparency: Adjust HMA fill transparency (default: 70)
Dashboard Position: Top-right, top-left, etc.
Color Theme: Choose color scheme
How to Use This Indicator
Step 1: Check HMA Ribbon Alignment
Look for full alignment (all 5 HMAs in order). Full alignment indicates strong, high-quality trend worth following.
Step 2: Verify EMA Cloud Direction
Ensure EMA cloud supports HMA direction. Bullish HMA + bullish EMA cloud = strong confirmation.
Step 3: Check Key MA Position
Verify price is above SMA 50 and SMA 200 for long trades, below for short trades. Golden Cross adds significant bullish weight.
Step 4: Review Trend Strength
Check dashboard trend strength percentage. Above 70% indicates strong trend, below 40% suggests caution.
Step 5: Assess Confluence Score
Review confluence score. Scores above +7 indicate strong multi-system alignment. Scores near 0 suggest mixed signals.
Step 6: Watch for Crossovers
Monitor crossover markers. Golden/Death Cross are major signals. HMA crossovers provide early trend change warnings.
Best Practices
Use on 1-hour to daily timeframes for optimal trend identification
Full HMA alignment (5/5) produces highest-quality trend-following opportunities
EMA cloud acts as dynamic support/resistance - use for entry refinement
Golden Cross with full HMA alignment = extremely strong bullish setup
Trend strength above 80% suggests strong trend continuation potential
Confluence score above +8 indicates rare, high-probability trend alignment
HMA crossovers provide early warnings but confirm with other layers
Wide ribbon spacing indicates strong momentum, narrow spacing suggests consolidation
Combine with price action and key levels for precise entries
Indicator Limitations
Moving averages are lagging indicators - trends confirmed after they've started
HMA crossovers can produce false signals in choppy markets
Full alignment is rare - waiting only for perfect setups may miss opportunities
Trend strength can remain high even as trend is ending
Golden/Death Cross signals are very lagging (occur well after trend change)
Multiple MAs can clutter chart - adjust display settings as needed
Confluence score is mathematical calculation, not prediction
Strong trends can reverse suddenly despite high trend strength scores
Requires understanding of moving average concepts for effective use
Technical Implementation
Built with Pine Script v6 using:
Custom Hull Moving Average calculation with WMA and square root period
5-layer HMA ribbon with gradient fills
EMA cloud with dynamic coloring
Key institutional MA tracking (SMA 50/200, EMA 200)
Multiple crossover detection systems
Comprehensive trend strength algorithm
Confluence scoring with weighted components
9-row dashboard with real-time metrics
Alert conditions for all major crossovers
The code is fully open-source and can be modified to adjust MA periods, colors, and dashboard layout.
Originality Statement
This indicator is original in its multi-layer moving average integration approach. While individual components (HMA, EMA cloud, SMA 50/200, crossovers) are established tools, this mashup is justified because:
It combines three different MA calculation methods (HMA, EMA, SMA) that respond differently to price
5-layer HMA ribbon provides granular trend quality assessment
Trend strength algorithm quantifies alignment across all 9 moving averages
Confluence scoring shows agreement between different MA systems
Integration of micro (HMA/EMA) and macro (SMA 50/200) trend perspectives
Comprehensive dashboard presents complex multi-MA data clearly
Each MA type contributes unique information: HMAs provide responsive trend indication with minimal lag, EMAs show short-term momentum, and SMAs provide institutional reference levels. The mashup's value lies in showing when these different calculation methods align, indicating genuine trend strength across multiple mathematical approaches and timeframes.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Moving averages are lagging indicators that confirm trends after they've begun. They do not predict future price movement. Strong trends can reverse suddenly, and high trend strength scores do not guarantee trend continuation. Golden Cross and Death Cross signals are very lagging and trends may be well-established before these signals occur.
The trend strength and confluence scores are mathematical calculations based on current MA positions, not predictions of future price movement. Past trend strength does not guarantee future performance. Market conditions change, and trends that appear strong can reverse without warning.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Elite Session Volume Distribution Engine [JOAT]Elite Session Volume Distribution Engine
Introduction
The Elite Session Volume Distribution Engine is an open-source indicator that combines session-based analysis (London, New York, Asian sessions) with volume distribution profiling, VWAP analysis, volume-weighted momentum indicators, and session high/low tracking. This mashup creates a comprehensive session and volume analysis system designed to identify when institutional volume enters the market during specific trading sessions and how that volume is distributed across price levels.
The indicator addresses a critical market reality: different trading sessions have distinct volume characteristics and institutional participation levels. London and New York sessions typically have highest volume and volatility, while Asian session is quieter. By tracking volume distribution, momentum, and key levels within each session, this tool helps traders identify optimal trading windows and understand how institutional volume shapes price action during different global market hours.
Chart showing session boxes, volume distribution, and VWAP on 45M timeframe
Why This Mashup Exists
This indicator combines five analytical frameworks that address different aspects of session-based trading:
Session Identification: Tracks London, New York, and Asian trading sessions
Volume Distribution: Analyzes how volume is distributed across price levels within sessions
VWAP Analysis: Calculates session-specific Volume Weighted Average Price
Volume Momentum: Tracks volume trends and climax conditions
Session High/Low: Identifies key levels established during each session
Each component serves a specific purpose: Session identification shows when institutional traders are active, Volume Distribution reveals where volume concentrates (value areas), VWAP shows institutional average price, Volume Momentum identifies accumulation/distribution phases, and Session High/Low marks key reference levels. Together, they create a complete picture of how institutional volume flows through different trading sessions.
The mashup is justified because these components work together in session-based trading: institutions enter during specific sessions (London/NY), create volume distribution patterns at key levels, establish VWAP as benchmark, show momentum through volume trends, and set session highs/lows that become support/resistance. Tracking all simultaneously reveals the complete session-based institutional flow.
Core Components Explained
1. Session Identification System
The indicator identifies three major trading sessions:
// London Session (03:00-12:00 GMT)
londonSession = input.session("0300-1200", "London Session")
inLondonSession = not na(time(timeframe.period, londonSession))
// New York Session (08:30-17:00 EST)
nySession = input.session("0830-1700", "NY Session")
inNYSession = not na(time(timeframe.period, nySession))
// Asian Session (00:00-09:00 GMT)
asianSession = input.session("0000-0900", "Asian Session")
inAsianSession = not na(time(timeframe.period, asianSession))
// Session overlap (London + NY)
sessionOverlap = inLondonSession and inNYSession
Session characteristics:
London Session: High volume, major currency pairs active, trend establishment
NY Session: Highest volume, US markets active, major moves occur
Asian Session: Lower volume, range-bound often, JPY pairs active
London/NY Overlap: Highest volume period, most volatile, best liquidity
The indicator can optionally display session boxes as background colors (disabled by default to reduce clutter).
2. Volume Distribution Analysis
Volume distribution shows where volume concentrates within price ranges:
// Calculate volume at different price levels
volumeAtPrice = array.new_float()
// For each price level in session range
for i = sessionLow to sessionHigh by tickSize
volumeAtLevel = sum of volume where price traded at level i
array.push(volumeAtPrice, volumeAtLevel)
// Identify Point of Control (POC) - price level with most volume
poc = price level with maximum volume
// Identify Value Area (VA) - price range containing 70% of volume
valueAreaHigh = upper bound of 70% volume
valueAreaLow = lower bound of 70% volume
Volume Distribution concepts:
Point of Control (POC): Price level with highest volume - strong support/resistance
Value Area High (VAH): Upper bound of 70% volume distribution
Value Area Low (VAL): Lower bound of 70% volume distribution
High Volume Nodes: Price levels with significant volume - support/resistance zones
Low Volume Nodes: Price levels with little volume - price moves through quickly
The indicator plots volume distribution as a histogram or profile showing where institutional volume concentrated during the session.
3. Session-Specific VWAP
VWAP resets at the start of each session:
// Session VWAP calculation
var float sessionVWAP = na
var float cumulativeTPV = 0.0 // Typical Price * Volume
var float cumulativeVol = 0.0
if session_start
cumulativeTPV := 0.0
cumulativeVol := 0.0
typicalPrice = (high + low + close) / 3
cumulativeTPV := cumulativeTPV + (typicalPrice * volume)
cumulativeVol := cumulativeVol + volume
sessionVWAP = cumulativeTPV / cumulativeVol
Session VWAP significance:
Institutional traders use VWAP as execution benchmark
Price above session VWAP = buyers in control during session
Price below session VWAP = sellers in control during session
VWAP acts as dynamic support/resistance within session
Distance from VWAP indicates overextension
The indicator plots session VWAP with dynamic coloring based on price position.
4. Volume Momentum Analysis
Volume momentum tracks institutional accumulation/distribution:
// Volume moving average
volumeMA = ta.sma(volume, 20)
// Volume classification
highVolume = volume > volumeMA * 1.5
veryHighVolume = volume > volumeMA * 2.0
climaxVolume = volume > volumeMA * 3.0
// Volume trend
volumeRising = volume > volume and volume > volume
volumeFalling = volume < volume and volume < volume
// Accumulation/Distribution
accumulation = close > open and highVolume and volumeRising
distribution = close < open and highVolume and volumeRising
// Volume momentum indicator
volumeMomentum = (volume - volumeMA) / volumeMA * 100
Volume Momentum signals:
Rising Volume + Up Close: Accumulation - bullish
Rising Volume + Down Close: Distribution - bearish
Climax Volume: Potential exhaustion or strong institutional move
Declining Volume: Lack of institutional interest
Volume Momentum > 50%: Very strong institutional participation
The indicator plots volume bars with color coding based on momentum and direction.
5. Session High/Low Tracking
Session highs and lows become important reference levels:
// Track current session high/low
var float currentSessionHigh = na
var float currentSessionLow = na
if session_start
currentSessionHigh := high
currentSessionLow := low
else
currentSessionHigh := math.max(currentSessionHigh, high)
currentSessionLow := math.min(currentSessionLow, low)
// Previous session levels
prevSessionHigh = currentSessionHigh
prevSessionLow = currentSessionLow
Session High/Low significance:
Current session high/low show intraday range
Previous session levels act as support/resistance
Breaks above previous session high = bullish continuation
Breaks below previous session low = bearish continuation
Session range size indicates volatility and institutional activity
The indicator plots only CURRENT session high/low (2 lines instead of 6) to keep chart clean. Previous session levels can be toggled on if needed.
Example showing session VWAP, volume distribution, and session high/low levels
Volume Distribution Dashboard
The dashboard (bottom-right position) displays:
Current Session: London/NY/Asian/Overlap
Session VWAP: Current VWAP value
Price vs VWAP: Distance from VWAP in %
POC: Point of Control price level
Value Area: VAH and VAL levels
Volume Status: High/Normal/Low relative to average
Volume Momentum: Rising/Falling/Climax
Session Range: High - Low distance
Accumulation/Distribution: Current phase
Visual Elements
Session Boxes: Optional background colors for each session (default: OFF)
Session VWAP: Dynamic line with color based on price position
Session High/Low: Horizontal lines for current session (2 lines only)
Volume Bars: Color-coded based on momentum and direction
Volume Distribution Profile: Histogram showing volume at price levels
POC Line: Horizontal line at Point of Control
Value Area: Shaded zone between VAH and VAL
Accumulation/Distribution Markers: Labels for strong volume phases
Dashboard: Bottom-right table with session and volume metrics
Chart demonstrating session VWAP, volume bars, and dashboard
How Components Work Together
The mashup reveals session-based institutional flow:
Session Trading Sequence:
1. Session Opens: New session begins (London/NY/Asian)
2. VWAP Establishes: Session VWAP forms as volume enters
3. Volume Distribution: Institutions create volume at key levels (POC, Value Area)
4. Session Range: High and low established through institutional activity
5. Volume Momentum: Accumulation or distribution phase identified
6. Session Close: Levels become reference for next session
Example: London session opens, price trades above session VWAP with rising volume (accumulation). Volume distribution shows POC forming at 1.2500 level. Session high reaches 1.2550. NY session opens, price respects London session high and VWAP, continues higher with climax volume. Dashboard shows strong accumulation with volume momentum +75%.
Input Parameters
Session Settings:
London Session: Time range (default: 0300-1200)
NY Session: Time range (default: 0830-1700)
Asian Session: Time range (default: 0000-0900)
Show Session Boxes: Toggle background colors (default: OFF)
Highlight Overlap: Emphasize London/NY overlap (default: enabled)
VWAP Settings:
Show Session VWAP: Toggle VWAP line (default: enabled)
VWAP Reset: Session, Daily, Weekly (default: Session)
VWAP Bands: Optional standard deviation bands (default: disabled)
Distance Alert: Alert when price moves X% from VWAP (default: 2%)
Volume Settings:
Volume MA Length: Period for volume average (default: 20)
High Volume Threshold: Multiplier for high volume (default: 1.5x)
Climax Volume Threshold: Multiplier for climax (default: 3.0x)
Show Volume Bars: Color-coded volume bars (default: enabled)
Show Distribution Profile: Volume at price histogram (default: enabled)
Session Levels:
Show Current Session H/L: Toggle current session levels (default: enabled)
Show Previous Session H/L: Toggle previous session levels (default: disabled)
Show POC: Toggle Point of Control line (default: enabled)
Show Value Area: Toggle VAH/VAL zone (default: enabled)
Display Options:
Show Dashboard: Toggle metrics table (default: enabled)
Dashboard Position: Bottom-right, top-right, etc. (default: bottom-right)
Color Theme: Choose color scheme
Transparency: Adjust visual element transparency
How to Use This Indicator
Step 1: Identify Active Session
Check dashboard to see which session is active. Focus trading during London and NY sessions for highest volume and best opportunities.
Step 2: Monitor Session VWAP
Use session VWAP as directional bias. Price above VWAP = bullish bias, below = bearish bias. VWAP often acts as support/resistance.
Step 3: Check Volume Distribution
Identify POC and Value Area. These levels often provide strong support/resistance. Price tends to return to POC (fair value).
Step 4: Assess Volume Momentum
Check if volume is rising (accumulation/distribution) or falling (lack of interest). Climax volume often marks important turning points.
Step 5: Use Session High/Low
Current session high/low define intraday range. Breaks above/below these levels signal potential breakout moves.
Step 6: Watch for Session Transitions
Session opens and closes often bring volatility. London open and NY open are particularly important for major moves.
Best Practices
Use on 5-minute to 1-hour timeframes for optimal session analysis
London/NY overlap (08:30-12:00 EST) offers highest volume and best opportunities
Session VWAP acts as magnet - price often returns to it
POC from previous session often becomes support/resistance in current session
Climax volume at session high/low often marks reversal points
Accumulation during Asian session often leads to breakout during London open
Value Area breaks signal strong directional moves
Previous session high/low become key levels for current session
Combine session analysis with other technical tools for best results
Indicator Limitations
Session times are fixed and may not account for daylight saving time changes
Volume distribution requires sufficient data within session to be meaningful
VWAP can be less relevant in very volatile or trending markets
Session high/low can be broken multiple times in volatile conditions
Lower timeframes may show choppy session transitions
Volume data quality varies across different markets and brokers
Asian session analysis less reliable due to lower volume
Requires understanding of session-based trading concepts
Visual elements can clutter chart if all options enabled
Technical Implementation
Built with Pine Script v6 using:
Session detection using time() function with session strings
Session-specific VWAP calculation with reset logic
Volume distribution profiling with POC and Value Area calculation
Volume momentum tracking with MA comparison
Session high/low tracking with persistent variables
Accumulation/distribution detection using volume and price
Dynamic dashboard with real-time session metrics
Optional session boxes with transparency control
Color-coded volume bars based on momentum
The code is fully open-source and can be modified to adjust session times, volume thresholds, and visual preferences.
Originality Statement
This indicator is original in its comprehensive session and volume integration approach. While individual components (session identification, VWAP, volume distribution, volume momentum, session high/low) are established concepts, this mashup is justified because:
It combines session-based analysis with volume distribution profiling
Session-specific VWAP provides more relevant institutional benchmark than daily VWAP
Integration of volume momentum with session context reveals accumulation/distribution phases
Simplified visual presentation (current session H/L only) reduces clutter
Dashboard presents complex session and volume data clearly
Focus on institutional trading sessions (London/NY) aligns with volume reality
Each component contributes unique information: Session identification shows when institutions are active, Volume Distribution reveals where they're trading, VWAP shows their average price, Volume Momentum shows their intent, and Session High/Low marks their range. The mashup's value lies in presenting these complementary session-based perspectives simultaneously, allowing traders to understand how institutional volume flows through different global trading sessions.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Session-based analysis and volume distribution are analytical tools that analyze past data. They do not predict future price movement or guarantee that institutional traders are active at identified levels. Market conditions change, and session patterns that worked historically may not work in the future.
VWAP and volume distribution levels can fail to provide support/resistance. Session highs and lows can be broken without leading to sustained moves. Volume momentum can change rapidly. Past session behavior does not guarantee future session behavior.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Smart Money Tracker [JOAT]Smart Money Tracker
Introduction
The Smart Money Tracker is an open-source indicator that combines institutional order flow concepts including Fair Value Gaps (FVG), Order Blocks (OB), Breaker Blocks, Liquidity Sweeps, Market Structure Breaks, and Displacement patterns. This mashup creates a comprehensive Smart Money Concepts (SMC) analysis system designed to identify where institutional traders are positioning themselves and how they manipulate price to accumulate or distribute positions.
The indicator addresses a fundamental market reality: institutional traders with large capital cannot simply buy or sell at market prices without moving the market against themselves. They must use sophisticated techniques including liquidity sweeps, gap creation, and order block manipulation. By tracking these institutional footprints simultaneously, this tool helps retail traders align with smart money rather than becoming their liquidity.
Chart showing FVG zones, Order Blocks, liquidity sweeps, and market structure on 15M timeframe
Why This Mashup Exists
This indicator combines six Smart Money Concepts that reveal different aspects of institutional behavior:
Fair Value Gaps (FVG): Inefficient price delivery zones where institutions moved price quickly
Order Blocks (OB): Last opposite-direction move before impulse, showing accumulation/distribution
Breaker Blocks: Failed Order Blocks that signal potential trend reversal
Liquidity Sweeps: Stop hunts where institutions trigger retail stops before real move
Market Structure: Break of Structure (BOS) and Change of Character (CHoCH) patterns
Displacement: Strong institutional moves with high volume and large candles
Each concept reveals different institutional tactics: FVGs show where they moved fast, Order Blocks show where they accumulated, Breaker Blocks show failed accumulation, Liquidity Sweeps show stop hunts, Market Structure shows control shifts, and Displacement shows strong directional intent. Together, they create a complete picture of institutional order flow that no single concept can provide.
The mashup is justified because these concepts work together in institutional trading sequences: institutions sweep liquidity, create FVGs during displacement, leave Order Blocks at accumulation zones, and break market structure when taking control. Tracking all simultaneously reveals the complete institutional playbook.
Core Components Explained
1. Fair Value Gap (FVG) Detection
FVGs occur when price moves so quickly that it leaves an unfilled gap:
// Bullish FVG: Current low > high from 2 bars ago
bullishFVG = low > high and close > high
fvgTop = low
fvgBottom = high
fvgSize = ((fvgTop - fvgBottom) / fvgBottom) * 100
// Bearish FVG: Current high < low from 2 bars ago
bearishFVG = high < low and close < low
fvgTop = low
fvgBottom = high
fvgSize = ((fvgTop - fvgBottom) / fvgBottom) * 100
FVG significance:
Represents inefficient price delivery - institutions moved too fast
Price often returns to "fill" these gaps before continuing
Larger FVGs (> 0.5%) are more significant
FVGs act as support/resistance zones
Multiple unfilled FVGs suggest strong directional intent
The indicator draws boxes for FVGs and tracks when they get "mitigated" (price returns to fill them). Timeframe-adaptive limits prevent clutter (fewer boxes on higher timeframes).
2. Order Block Identification
Order Blocks mark where institutions accumulated or distributed positions:
// Bullish Order Block
// Two consecutive bearish candles + strong bullish candle with high volume
bullishOB = close < open and
close < open and
close > open and
volume > volumeMA * 1.5
obHigh = high
obLow = low
// Bearish Order Block
// Two consecutive bullish candles + strong bearish candle with high volume
bearishOB = close > open and
close > open and
close < open and
volume > volumeMA * 1.5
Order Block characteristics:
Last opposite-direction move before strong impulse
Represents institutional accumulation (bullish OB) or distribution (bearish OB)
Often provides support/resistance on retests
Volume confirmation ensures institutional participation
Stronger OBs have larger candles and higher volume
The indicator draws solid boxes for Order Blocks and tracks their strength based on volume and candle size. Timeframe-adaptive filtering ensures only significant OBs are displayed.
3. Breaker Block Detection
Breaker Blocks are failed Order Blocks that signal potential reversals:
// Track last bullish and bearish Order Block levels
var float lastBullOBHigh = na
var float lastBearOBLow = na
if bullishOB
lastBullOBHigh := high
if bearishOB
lastBearOBLow := low
// Breaker Bull: Price breaks above failed bearish OB
breakerBull = not na(lastBearOBLow) and
close > lastBearOBLow and
close <= lastBearOBLow
// Breaker Bear: Price breaks below failed bullish OB
breakerBear = not na(lastBullOBHigh) and
close < lastBullOBHigh and
close >= lastBullOBHigh
Breaker Block significance:
Failed Order Blocks often become strong support/resistance in opposite direction
Indicate institutional position reversal
High-probability reversal zones when combined with other SMC signals
Often mark major trend changes
The indicator marks Breaker Blocks with "BB" labels and tracks them as potential reversal zones.
4. Liquidity Sweep Analysis
Liquidity Sweeps identify stop hunts before real moves:
lookbackBars = 20
// Recent highs and lows (liquidity pools)
recentHigh = ta.highest(high, lookbackBars)
recentLow = ta.lowest(low, lookbackBars)
// Liquidity Sweep High (stop hunt above recent high)
liquiditySweepHigh = high > recentHigh and
close < recentHigh and
volume > volumeMA * 1.5
// Liquidity Sweep Low (stop hunt below recent low)
liquiditySweepLow = low < recentLow and
close > recentLow and
volume > volumeMA * 1.5
// Strong sweeps have higher volume
strongSweep = volume > volumeMA * 2.5
Liquidity Sweep characteristics:
Price briefly exceeds recent high/low to trigger stops
Closes back inside range - "fake breakout"
High volume confirms institutional participation
Often precedes strong moves in opposite direction
"Strong" sweeps (very high volume) are more reliable
The indicator places "LIQ" and "STRONG LIQ" labels precisely at sweep tips (above bars for high sweeps, below bars for low sweeps) with timeframe-adaptive spacing to prevent overlap.
5. Market Structure Analysis
Market structure tracks control shifts between buyers and sellers:
// Break of Structure (BOS)
// Price breaks beyond previous swing high/low in trend direction
bullishBOS = close > ta.highest(high , 20) and trend == bullish
bearishBOS = close < ta.lowest(low , 20) and trend == bearish
// Change of Character (CHoCH)
// Price breaks structure against trend - potential reversal
bullishCHoCH = close > ta.highest(high , 20) and trend == bearish
bearishCHoCH = close < ta.lowest(low , 20) and trend == bullish
Market Structure significance:
BOS confirms trend continuation
CHoCH signals potential trend reversal
Helps identify when institutional control shifts
Provides context for other SMC signals
The indicator marks BOS and CHoCH with labels and uses them to determine overall market bias.
6. Displacement Detection
Displacement identifies strong institutional moves:
atr = ta.atr(14)
// Displacement: Large candle (> 2x ATR) with climax volume
displacement = math.abs(close - open) > atr * 2 and
volume > volumeMA * 3.0
bullishDisplacement = displacement and close > open
bearishDisplacement = displacement and close < open
Displacement characteristics:
Very large candles relative to ATR
Climax volume (> 3x average)
Indicates strong institutional directional intent
Often creates FVGs
Signals potential trend acceleration
The indicator marks displacement with "DISP" labels and uses them to identify high-conviction institutional moves.
Example showing all SMC concepts: FVGs, Order Blocks, Breaker Blocks, and liquidity sweeps
Timeframe-Adaptive System
The indicator automatically adjusts based on timeframe to prevent clutter:
// Higher timeframes (2H+): Fewer boxes, larger minimum sizes
if timeframe >= 120 minutes:
maxFVGs = 12
maxOBs = 10
minFVGSize = 0.5%
minOBSize = 0.8%
labelSpacing = 15 bars
// Medium timeframes (1H): Moderate filtering
else if timeframe >= 60 minutes:
maxFVGs = 15
maxOBs = 12
minFVGSize = 0.4%
minOBSize = 0.6%
labelSpacing = 12 bars
// Lower timeframes (15M): More boxes, smaller minimum sizes
else:
maxFVGs = 20-25
maxOBs = 15-20
minFVGSize = 0.3%
minOBSize = 0.4%
labelSpacing = 8-10 bars
This ensures the indicator remains useful across all timeframes without overwhelming the chart.
SMC Confluence Dashboard
The dashboard (top-right position) displays:
Market Bias: Bullish/Bearish/Neutral based on structure
Active FVGs: Count of unfilled Fair Value Gaps
Active OBs: Count of untested Order Blocks
Recent Sweeps: Liquidity sweeps in last 50 bars
Structure: Last BOS or CHoCH type
Displacement: Recent displacement direction
SMC Score: Overall confluence (0-10)
SMC Score calculation:
SMC Score Components:
- Significant FVG present: +2 points
- Strong Order Block present: +2 points
- Breaker Block active: +1 point
- Recent liquidity sweep: +2 points
- Displacement in direction: +3 points
Total: 0-10 points
Visual Elements
FVG Boxes: Green (bullish) and red (bearish) boxes, removed when mitigated
Order Block Boxes: Solid green/red boxes with strength-based transparency
Breaker Block Labels: "BB" markers at breaker zones
Liquidity Sweep Labels: "LIQ" and "STRONG LIQ" at sweep tips
Displacement Labels: "DISP" markers on displacement candles
Structure Labels: "BOS" and "CHoCH" at structure breaks
Mitigation Markers: Small circles when FVGs get filled
Dashboard: Top-right table with SMC metrics
How Components Work Together
The mashup reveals institutional trading sequences:
Sequence 1 - Accumulation:
1. Liquidity Sweep triggers retail stops
2. Order Block forms as institutions accumulate
3. Displacement occurs as institutions push price
4. FVG created during fast move
5. BOS confirms trend direction
Sequence 2 - Reversal:
1. Multiple liquidity sweeps fail to extend trend
2. Order Block fails, becomes Breaker Block
3. CHoCH signals control shift
4. Opposite-direction displacement
5. New trend structure forms
Example: Price sweeps below recent lows (liquidity sweep), then strongly reverses with high volume (displacement), leaving a bullish FVG. A bullish Order Block forms at the reversal zone. Price breaks above previous structure (BOS). SMC Score reaches 9/10, signaling strong bullish institutional setup.
Input Parameters
FVG Settings:
Show FVGs: Toggle FVG boxes (default: enabled)
Min FVG Size: Minimum gap size % (default: 0.3%)
Max FVG Boxes: Limit displayed boxes (default: timeframe-adaptive)
Show Mitigation: Mark when FVGs get filled (default: enabled)
Order Block Settings:
Show Order Blocks: Toggle OB boxes (default: enabled)
Min OB Strength: Minimum volume multiplier (default: 1.5x)
Max OB Boxes: Limit displayed boxes (default: timeframe-adaptive)
OB Lookback: Bars to track OBs (default: 100)
Liquidity Settings:
Show Liquidity Sweeps: Toggle sweep labels (default: enabled)
Lookback Bars: Period for liquidity pools (default: 20)
Strong Sweep Threshold: Volume multiplier (default: 2.5x)
Label Spacing: Minimum bars between labels (default: timeframe-adaptive)
Structure Settings:
Show Structure: Toggle BOS/CHoCH labels (default: enabled)
Show Breaker Blocks: Toggle BB labels (default: enabled)
Show Displacement: Toggle DISP labels (default: enabled)
Structure Sensitivity: Swing detection period (default: 20)
Display Options:
Show Dashboard: Toggle SMC dashboard (default: enabled)
Timeframe Adaptive: Auto-adjust limits (default: enabled)
Remove Extensions: Don't extend boxes right (default: enabled)
Color Theme: Choose color scheme
How to Use This Indicator
Step 1: Identify Market Structure
Check for recent BOS or CHoCH. BOS suggests trend continuation, CHoCH suggests potential reversal.
Step 2: Look for Liquidity Sweeps
Liquidity sweeps often precede strong moves in opposite direction. "STRONG LIQ" sweeps are particularly significant.
Step 3: Identify Order Blocks
Look for Order Blocks in the direction of intended trade. OBs often provide high-probability entry zones on retests.
Step 4: Check for FVGs
Unfilled FVGs act as magnets - price often returns to fill them. Can be used for entry targets or profit-taking zones.
Step 5: Watch for Displacement
Displacement signals strong institutional intent. When displacement occurs from an Order Block, it confirms the zone's validity.
Step 6: Monitor Breaker Blocks
Failed Order Blocks (Breaker Blocks) often mark major reversals. These are high-probability reversal zones.
Step 7: Review SMC Score
Check dashboard SMC Score. Scores above 7 indicate strong institutional confluence.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal SMC signal quality
Liquidity sweeps followed by displacement are extremely high-probability setups
Order Block retests with FVG confluence provide excellent risk:reward entries
Wait for price to return to Order Blocks rather than chasing displacement
Multiple unfilled FVGs in same direction suggest strong institutional intent
Breaker Blocks combined with CHoCH signal major trend reversals
Higher timeframe SMC signals are more reliable than lower timeframe
Use SMC Score as filter - focus on setups with 7+ score
Combine with traditional support/resistance for additional confirmation
Indicator Limitations
Not all FVGs get filled - some remain unfilled in strong trends
Order Blocks don't always provide support/resistance on retest
Liquidity sweeps can be followed by additional sweeps (multiple stop hunts)
Timeframe-adaptive filtering may hide some valid signals
Requires understanding of Smart Money Concepts for effective use
Visual elements can clutter chart even with adaptive limits
SMC concepts work best in trending markets, less effective in ranges
Institutional behavior patterns can change over time
No SMC system eliminates false signals entirely
Technical Implementation
Built with Pine Script v6 using:
Box management system with automatic cleanup
Timeframe-adaptive limits and filtering
Anti-overlap logic for all labels with dynamic spacing
FVG mitigation tracking with visual markers
Order Block strength calculation based on volume and size
Liquidity pool identification with sweep detection
Market structure tracking with BOS/CHoCH logic
Displacement detection using ATR and volume
Real-time SMC confluence scoring
Comprehensive dashboard with all SMC metrics
The code is fully open-source and can be modified to adjust thresholds, visual preferences, and filtering criteria.
Originality Statement
This indicator is original in its comprehensive SMC integration approach. While individual concepts (FVG, Order Blocks, Breaker Blocks, Liquidity Sweeps, Market Structure, Displacement) are established Smart Money Concepts, this mashup is justified because:
It tracks all major SMC concepts simultaneously in one indicator
Timeframe-adaptive system prevents clutter while maintaining functionality
Anti-overlap logic ensures clean visual presentation
SMC confluence scoring quantifies institutional setup quality
Integration reveals complete institutional trading sequences
Enhanced visual elements (precise label positioning, mitigation markers) improve usability
Each SMC concept reveals different institutional behavior: FVGs show fast moves, Order Blocks show accumulation, Breaker Blocks show failed accumulation, Liquidity Sweeps show stop hunts, Market Structure shows control shifts, and Displacement shows strong intent. The mashup's value lies in presenting these complementary institutional footprints simultaneously, allowing traders to identify complete institutional trading sequences rather than isolated signals.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Smart Money Concepts are analytical frameworks based on observations of institutional trading patterns. They do not guarantee that institutions are actually trading at identified zones, nor do they predict future institutional behavior. Market conditions change, and patterns that worked historically may not work in the future.
The SMC Score is a mathematical calculation based on current market structure, not a prediction of future price movement. High SMC scores do not ensure profitable trades. Order Blocks, FVGs, and other SMC zones can fail to provide support/resistance. Liquidity sweeps can be followed by additional sweeps.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Advanced Divergence Hunter [JOAT]Advanced Divergence Hunter
Introduction
The Advanced Divergence Hunter is an open-source multi-oscillator indicator that simultaneously tracks divergences across six different momentum indicators: RSI, MACD, Stochastic RSI, CCI, MFI, and Williams %R. This mashup creates a comprehensive divergence detection system designed to identify momentum exhaustion and potential reversals by analyzing when multiple oscillators simultaneously show divergence from price action.
The indicator addresses a critical limitation of single-oscillator divergence detection: false signals. By requiring confluence across multiple oscillators using different calculation methods, this tool significantly reduces false divergence signals and highlights only the most reliable momentum exhaustion patterns that occur when price and momentum fundamentally disconnect across multiple measurement frameworks.
Chart showing multiple divergence signals across oscillators with dashboard on 1H timeframe
Why This Mashup Exists
This indicator combines six oscillators that detect divergences using fundamentally different methodologies:
RSI: Momentum oscillator based on average gains vs losses
MACD: Trend-following momentum using EMA convergence/divergence
Stochastic RSI: Stochastic calculation applied to RSI for enhanced sensitivity
CCI (Commodity Channel Index): Measures deviation from statistical mean
MFI (Money Flow Index): Volume-weighted RSI showing buying/selling pressure
Williams %R: Momentum indicator measuring overbought/oversold using highest high/lowest low
Each oscillator responds to different market dynamics: RSI tracks momentum speed, MACD shows trend strength changes, Stochastic RSI catches early shifts, CCI identifies statistical extremes, MFI incorporates volume, and Williams %R uses price extremes. When multiple oscillators show divergence simultaneously, it indicates genuine momentum exhaustion rather than noise from a single calculation method.
The mashup is justified because these oscillators use distinct mathematical approaches (rate of change, moving average convergence, stochastic, statistical deviation, volume-weighted, price extremes) that respond to different aspects of price movement. Confluence across multiple methods provides significantly higher reliability than any single divergence signal.
Example showing all six oscillators with divergence markers and alignment indicators
Core Components Explained
1. RSI Divergence Detection
Standard RSI calculation with pivot-based divergence logic:
rsi = ta.rsi(close, 14)
// Identify swing points
pivotHigh = ta.pivothigh(rsi, 5, 5)
pivotLow = ta.pivotlow(rsi, 5, 5)
// Regular Bullish Divergence
// Price: Lower Low, RSI: Higher Low
bullDiv = priceLow < prevPriceLow AND rsiLow > prevRSILow
// Regular Bearish Divergence
// Price: Higher High, RSI: Lower High
bearDiv = priceHigh > prevPriceHigh AND rsiHigh < prevRSIHigh
2. MACD Histogram Divergence
MACD histogram divergences often lead price divergences:
= ta.macd(close, 12, 26, 9)
// Histogram divergence detection
// Bullish: Price LL, Histogram HL
// Bearish: Price HH, Histogram LH
The indicator plots MACD histogram with enhanced visualization and tracks divergences separately from RSI.
3. Stochastic RSI Divergence
More sensitive than regular RSI, catches early momentum shifts:
stochRSI = ta.stoch(rsi, rsi, rsi, 14)
// K and D line divergences
// Often diverges before regular RSI
Stochastic RSI K and D lines are plotted, and divergences on both lines are tracked independently.
4. CCI Divergence Detection
CCI measures price deviation from statistical mean:
cci = ta.cci(close, 20)
// CCI divergences indicate statistical exhaustion
// Bullish: Price LL, CCI HL
// Bearish: Price HH, CCI LH
CCI divergences are particularly reliable at extreme levels (> +100 or < -100).
5. MFI Divergence (Volume-Weighted)
MFI incorporates volume, making divergences more significant:
// Calculate typical price
typicalPrice = (high + low + close) / 3
// Money flow with volume
rawMoneyFlow = typicalPrice * volume
// MFI calculation
mfi = 100 - (100 / (1 + positiveFlow / negativeFlow))
// Volume divergences often lead price
// Bullish: Price LL, MFI HL (buying pressure increasing)
// Bearish: Price HH, MFI LH (selling pressure increasing)
MFI divergences are weighted more heavily in the confluence system because they incorporate volume.
6. Williams %R Divergence
Williams %R uses highest high and lowest low:
williamsR = -100 * (ta.highest(high, 14) - close) / (ta.highest(high, 14) - ta.lowest(low, 14))
// Divergences at extreme levels (-80 to -100 or -20 to 0)
// Bullish: Price LL, Williams %R HL
// Bearish: Price HH, Williams %R LH
Williams %R divergences are most reliable when oscillator is in extreme zones.
Divergence Confluence System
The indicator tracks divergences across all six oscillators and calculates confluence:
Divergence Confluence Score:
- Single oscillator divergence: 1 point
- Two oscillators: 2 points
- Three oscillators: 4 points
- Four oscillators: 7 points
- Five oscillators: 11 points
- All six oscillators: 15 points (MEGA divergence)
Divergence classification:
Weak Divergence: 1-2 oscillators (score 1-2)
Moderate Divergence: 3 oscillators (score 4)
Strong Divergence: 4 oscillators (score 7)
Very Strong Divergence: 5 oscillators (score 11)
MEGA Divergence: All 6 oscillators (score 15)
The dashboard displays which oscillators are showing divergence and the total confluence score.
Oscillator Alignment Analysis
Beyond divergences, the indicator tracks oscillator alignment:
Alignment Score:
- RSI in healthy range (40-60 bull, 60-40 bear): +1
- MACD histogram direction: +1
- Stochastic RSI position: +1
- CCI direction: +1
- MFI level: +1
- Williams %R position: +1
Total Alignment: 0-6 points
Alignment interpretation:
5-6 aligned: Strong momentum consensus
3-4 aligned: Moderate momentum
0-2 aligned: Weak or conflicting momentum
Enhanced Dashboard System
The indicator features an 11-row dashboard showing:
Row 1: Overall momentum direction (BULL/BEAR/NEUTRAL)
Row 2: Divergence confluence score with color coding
Row 3: RSI value and divergence status
Row 4: MACD histogram status
Row 5: Stochastic RSI K value
Row 6: CCI value and status
Row 7: MFI value and divergence status
Row 8: Williams %R value
Row 9: Momentum strength (0-100)
Row 10: Oscillator alignment (X/6 aligned)
Row 11: Active divergences count
Dashboard showing divergence confluence with individual oscillator breakdown
Visual Elements
Oscillator Lines: All six oscillators plotted with distinct colors
Divergence Labels: "DIV" markers at divergence points, sized by confluence
Confluence Markers: Large diamond shapes for MEGA divergences (6/6)
Background Zones: Color-coded backgrounds for extreme conditions
Overbought/Oversold Lines: Reference levels for each oscillator
Zero/Midpoint Lines: Centerline references
Histogram Bars: MACD histogram with gradient coloring
Dashboard: Comprehensive table with all oscillator readings
How Components Work Together
The mashup creates layered divergence analysis:
Layer 1 - Individual Detection: Each oscillator independently detects divergences
Layer 2 - Confluence Calculation: System counts how many oscillators show divergence
Layer 3 - Weighting: Volume-based divergences (MFI) weighted more heavily
Layer 4 - Alignment Check: Verifies overall oscillator consensus
Layer 5 - Extreme Zones: Identifies when divergences occur at statistical extremes
Layer 6 - Signal Generation: Produces graded signals based on confluence strength
Example scenario: Price makes higher high, but RSI, MACD, Stochastic RSI, and MFI all make lower highs (4/6 divergence). CCI and Williams %R are in extreme overbought zones. Confluence score is 7 (Strong Divergence), and dashboard shows 4 active divergences. This signals high-probability bearish reversal setup.
Input Parameters
Oscillator Settings:
RSI Length: Period for RSI (default: 14)
MACD Settings: Fast 12, Slow 26, Signal 9
Stochastic RSI Length: Period for Stoch RSI (default: 14)
CCI Length: Period for CCI (default: 20)
MFI Length: Period for MFI (default: 14)
Williams %R Length: Period for Williams %R (default: 14)
Divergence Settings:
Pivot Lookback: Bars for pivot detection (default: 5)
Min Confluence: Minimum oscillators for signal (default: 3)
Show All Divergences: Display single-oscillator divergences (default: disabled)
Show Only Strong: Display only 4+ confluence (default: enabled)
Display Options:
Show Dashboard: Toggle dashboard (default: enabled)
Show Oscillators: Toggle oscillator plots (default: enabled)
Show Background Zones: Toggle extreme zone coloring (default: enabled)
Dashboard Position: Top-right, bottom-right, etc.
How to Use This Indicator
Step 1: Monitor Divergence Confluence
Watch the dashboard divergence score. Wait for 3+ oscillators showing divergence (score 4+) before considering reversal trades.
Step 2: Check Oscillator Extremes
Divergences are most reliable when oscillators are in extreme zones (RSI > 70 or < 30, MFI > 80 or < 20, etc.).
Step 3: Verify Alignment
Check oscillator alignment score. Low alignment (0-2) with high divergence confluence suggests strong reversal potential.
Step 4: Identify MEGA Divergences
When all 6 oscillators show divergence (MEGA), it signals extremely high probability reversal setup. These are rare but very reliable.
Step 5: Confirm with Price Action
Wait for price action confirmation (reversal candlestick patterns, trendline breaks) before entering trades based on divergences.
Step 6: Use for Exit Signals
If holding trend-following position and strong divergence appears, consider taking profits or tightening stops.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal divergence reliability
Wait for 3+ oscillator confluence before acting on divergence signals
MEGA divergences (6/6) are rare but extremely reliable - don't ignore them
MFI divergences are particularly significant because they incorporate volume
Divergences in strong trends often lead to pullbacks, not full reversals
Combine with support/resistance levels for precise entry timing
Hidden divergences signal trend continuation, regular divergences signal reversal
Multiple consecutive divergences increase reversal probability
Use oscillator alignment to gauge overall momentum health
Indicator Limitations
Divergences can persist for extended periods before reversal occurs
Strong trends can continue despite multiple oscillator divergences
Pivot-based detection means divergences are confirmed with lag
False divergences can occur in choppy, ranging markets
MEGA divergences are rare - waiting only for these may miss opportunities
Oscillator calculations vary in sensitivity - some may diverge prematurely
Requires understanding of each oscillator's characteristics
No divergence system eliminates false signals entirely
Performance varies across different markets and volatility regimes
Technical Implementation
Built with Pine Script v6 using:
Six independent oscillator calculations
Pivot-based divergence detection for each oscillator
Confluence scoring algorithm with weighted components
Oscillator alignment tracking system
Enhanced 11-row dashboard with real-time updates
Dynamic background zones for extreme conditions
Anti-overlap logic for divergence labels
Gradient coloring for MACD histogram
The code is fully open-source and can be modified to adjust oscillator parameters, confluence thresholds, and visual preferences.
Originality Statement
This indicator is original in its multi-oscillator divergence confluence approach. While individual oscillators (RSI, MACD, Stochastic RSI, CCI, MFI, Williams %R) are established tools, this mashup is justified because:
It tracks divergences across six oscillators using fundamentally different calculations
The confluence scoring system quantifies divergence strength across multiple methods
Integration of volume-weighted divergence (MFI) with price-based oscillators
Oscillator alignment analysis provides momentum consensus measurement
Enhanced dashboard presents complex multi-oscillator data clearly
MEGA divergence detection identifies extremely rare, high-probability setups
Each oscillator contributes unique divergence information: RSI shows momentum speed divergence, MACD shows trend strength divergence, Stochastic RSI catches early divergences, CCI shows statistical divergence, MFI shows volume-weighted divergence, and Williams %R shows price extreme divergence. The mashup's value lies in identifying when multiple independent calculation methods simultaneously show momentum exhaustion, significantly reducing false signals.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Divergence indicators are analytical tools that identify potential momentum exhaustion, not guarantees of reversals. Divergences can persist for extended periods, and strong trends can continue despite multiple divergence signals. Past divergence performance does not guarantee future results.
The confluence score is a mathematical calculation based on current oscillator readings, not a prediction of future price movement. High confluence scores do not ensure profitable trades. Market conditions change, and divergence patterns that worked historically may not work in the future.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Multi-Timeframe Strength Scanner [JOAT]Multi-Timeframe Strength Scanner
Introduction
The Multi-Timeframe Strength Scanner is an open-source indicator that combines higher timeframe trend analysis with current timeframe momentum indicators to create a comprehensive market strength assessment system. This mashup integrates ADX (Average Directional Index), Donchian Channels, VWAP (Volume Weighted Average Price), RSI divergence detection, and multi-timeframe EMA analysis into a unified scanner that identifies when trend strength aligns across multiple timeframes.
The indicator addresses a critical trading challenge: signals that look strong on one timeframe often fail because higher timeframes are moving in the opposite direction. By analyzing 15-minute, 1-hour, and 4-hour timeframes simultaneously while monitoring current timeframe momentum, this tool helps traders avoid counter-trend trades and identify high-probability setups where multiple timeframes align.
Chart showing multi-timeframe alignment dashboard and strength indicators on 15M timeframe
Why This Mashup Exists
This indicator combines five analytical frameworks that address different aspects of trend strength:
ADX Analysis: Measures trend strength regardless of direction using directional movement
Donchian Channels: Identifies breakouts and trend continuation using price extremes
VWAP: Shows institutional average price and volume-weighted fair value
RSI Divergence: Detects momentum exhaustion at current timeframe swing points
Multi-Timeframe EMAs: Confirms trend direction across 15M, 1H, and 4H timeframes
Each component serves a specific purpose: ADX quantifies trend strength, Donchian Channels identify breakout momentum, VWAP reveals institutional positioning, RSI divergences warn of reversals, and multi-timeframe EMAs ensure directional alignment. Together, they create a strength scanner that filters out weak, counter-trend setups and highlights only those with multi-timeframe confirmation.
The mashup is justified because these components use fundamentally different data (directional movement, price extremes, volume-weighted averages, momentum oscillators, moving averages) that respond to different market conditions. When they align, it indicates genuine trend strength rather than temporary momentum.
Core Components Explained
1. ADX Trend Strength System
ADX (Average Directional Index) measures trend strength on a scale of 0-100:
= ta.dmi(adxLength, adxLength)
// Trend strength classification
strongTrend = adx > adxThreshold // Default: 20
veryStrongTrend = adx > 40
extremeTrend = adx > 60
// Direction determination
bullishTrend = plus > minus
bearishTrend = minus > plus
ADX interpretation:
ADX < 20: Weak trend or ranging market - avoid trend-following strategies
ADX 20-40: Moderate trend strength - standard trend-following viable
ADX 40-60: Strong trend - high-probability trend continuation
ADX > 60: Extreme trend - potential exhaustion or very strong momentum
The indicator plots ADX as a line with color coding:
Green: Strong bullish trend (ADX > 20, +DI > -DI)
Red: Strong bearish trend (ADX > 20, -DI > +DI)
Gray: Weak trend or ranging (ADX < 20)
2. Donchian Channel Breakout System
Donchian Channels track the highest high and lowest low over a specified period:
donchianLength = 20 // Configurable
upperChannel = ta.highest(high, donchianLength)
lowerChannel = ta.lowest(low, donchianLength)
midChannel = (upperChannel + lowerChannel) / 2
Breakout signals:
Bullish Breakout: Close above upper channel = new 20-bar high
Bearish Breakout: Close below lower channel = new 20-bar low
Channel Position: Price near upper channel = bullish strength, near lower = bearish strength
The indicator uses Donchian breakouts to confirm trend strength. When price breaks out of the channel with strong ADX, it signals high-momentum trend continuation.
3. VWAP Analysis
VWAP (Volume Weighted Average Price) calculates the average price weighted by volume:
vwap = ta.vwap(hlc3)
// Position analysis
aboveVWAP = close > vwap // Bullish positioning
belowVWAP = close < vwap // Bearish positioning
// Distance from VWAP
vwapDistance = ((close - vwap) / vwap) * 100
VWAP significance:
Institutional traders use VWAP as benchmark for execution quality
Price above VWAP = buyers in control, institutions paying premium
Price below VWAP = sellers in control, institutions getting discount
Large distance from VWAP = potential mean reversion opportunity
VWAP acts as dynamic support/resistance level
The indicator plots VWAP with dynamic coloring based on price position and uses it for trend confirmation.
4. RSI Divergence Detection
The indicator detects divergences using pivot-based analysis:
rsi = ta.rsi(close, 14)
// Identify swing points
pivotHigh = ta.pivothigh(rsi, 5, 5)
pivotLow = ta.pivotlow(rsi, 5, 5)
// Compare current pivot with previous pivot
bullishDivergence = price makes lower low AND rsi makes higher low
bearishDivergence = price makes higher high AND rsi makes lower high
Divergence types:
Regular Bullish: Price LL, RSI HL - momentum improving, potential reversal up
Regular Bearish: Price HH, RSI LH - momentum deteriorating, potential reversal down
Hidden Bullish: Price HL, RSI LL - trend continuation signal in uptrend
Hidden Bearish: Price LH, RSI HH - trend continuation signal in downtrend
Divergences are marked with "DIV" labels and used to warn of potential trend exhaustion or continuation.
5. Multi-Timeframe EMA Analysis
The indicator analyzes trend direction across three higher timeframes:
// Request higher timeframe data
htf15mEMA = request.security(syminfo.tickerid, "15", ta.ema(close, 21))
htf1hEMA = request.security(syminfo.tickerid, "60", ta.ema(close, 21))
htf4hEMA = request.security(syminfo.tickerid, "240", ta.ema(close, 21))
// Determine trend direction
htf15mBullish = close > htf15mEMA
htf1hBullish = close > htf1hEMA
htf4hBullish = close > htf4hEMA
// Count aligned timeframes
bullishCount = (htf15mBullish ? 1 : 0) + (htf1hBullish ? 1 : 0) + (htf4hBullish ? 1 : 0)
bearishCount = (!htf15mBullish ? 1 : 0) + (!htf1hBullish ? 1 : 0) + (!htf4hBullish ? 1 : 0)
Alignment classification:
STRONG BULL: All 3 timeframes bullish (3/3 alignment)
BULL: 2 out of 3 timeframes bullish
MIXED: Timeframes conflicting (1-1-1 or 2-1 split)
BEAR: 2 out of 3 timeframes bearish
STRONG BEAR: All 3 timeframes bearish (3/3 alignment)
Example showing multi-timeframe alignment dashboard with all three timeframes bullish
Strength Scoring System
The indicator calculates a comprehensive strength score (0-100) by evaluating:
Strength Score Components:
- ADX Strength: Up to 25 points (ADX > 40 = 25, ADX > 20 = 15, ADX < 20 = 0)
- ADX Direction: Up to 15 points (+DI > -DI = 15 for bull, -DI > +DI = 15 for bear)
- Donchian Position: Up to 15 points (breakout = 15, near channel = 10, mid-channel = 5)
- VWAP Position: Up to 15 points (above VWAP = 15 for bull, below = 15 for bear)
- MTF Alignment: Up to 20 points (3/3 = 20, 2/3 = 13, 1/3 = 7)
- RSI Level: Up to 10 points (healthy range = 10, extreme = 5, divergence = -5)
Score interpretation:
80-100: Extremely strong trend - high-probability continuation
60-79: Strong trend - favorable for trend-following
40-59: Moderate trend - selective trend trades
20-39: Weak trend - caution, potential reversal
0-19: Very weak or counter-trend - avoid trend-following
The dashboard displays the strength score with color coding and individual component breakdown.
Visual Elements
ADX Line: Main trend strength indicator with dynamic coloring
+DI/-DI Lines: Directional movement indicators
ADX Threshold: Horizontal line at 20 (configurable)
Donchian Channels: Upper, middle, and lower channel lines
VWAP Line: Volume-weighted average price with dynamic coloring
Divergence Labels: "DIV" markers at RSI divergence points
Strength Bars: Background coloring based on strength score
Dashboard: Comprehensive table showing:
- Current strength score
- ADX value and direction
- Donchian position
- VWAP position
- MTF alignment (15M, 1H, 4H status)
- RSI level
- Overall trend classification
Chart showing strength dashboard with component breakdown and visual indicators
How Components Work Together
The mashup creates a layered strength analysis:
Layer 1 - Trend Strength: ADX quantifies how strong the trend is
Layer 2 - Breakout Momentum: Donchian Channels identify momentum surges
Layer 3 - Institutional Positioning: VWAP shows where smart money is positioned
Layer 4 - Momentum Health: RSI divergences warn of exhaustion
Layer 5 - Multi-Timeframe Confirmation: HTF EMAs ensure directional alignment
Layer 6 - Synthesis: Strength score combines all factors into actionable metric
Example scenario: ADX is 45 (Layer 1), price breaks above Donchian upper channel (Layer 2), trading above VWAP (Layer 3), no RSI divergence (Layer 4), and all three higher timeframes are bullish (Layer 5). The strength score reaches 90 (Layer 6), signaling extremely strong bullish trend with high continuation probability.
Input Parameters
ADX Settings:
ADX Length: Period for ADX calculation (default: 14)
ADX Threshold: Minimum ADX for strong trend (default: 20)
Show +DI/-DI: Toggle directional indicators (default: enabled)
Donchian Settings:
Donchian Length: Period for channel calculation (default: 20)
Show Channels: Toggle channel display (default: enabled)
Breakout Sensitivity: Threshold for breakout signals (default: close beyond channel)
VWAP Settings:
Show VWAP: Toggle VWAP line (default: enabled)
VWAP Reset: Session, Week, Month, or Never (default: Daily)
Distance Alert: Alert when price moves X% from VWAP (default: 2%)
RSI Settings:
RSI Length: Period for RSI calculation (default: 14)
Show Divergences: Toggle divergence markers (default: enabled)
Pivot Lookback: Bars for pivot detection (default: 5)
Multi-Timeframe Settings:
HTF 1: First higher timeframe (default: 15 minutes)
HTF 2: Second higher timeframe (default: 1 hour)
HTF 3: Third higher timeframe (default: 4 hours)
EMA Length: Period for HTF EMAs (default: 21)
Min Alignment: Minimum timeframes aligned for signal (default: 2/3)
Display Options:
Show Dashboard: Toggle strength score table (default: enabled)
Show Strength Bars: Toggle background coloring (default: enabled)
Dashboard Position: Top-right, top-left, bottom-right, bottom-left
Color Theme: Choose between multiple color schemes
How to Use This Indicator
Step 1: Check Multi-Timeframe Alignment
Review the dashboard MTF section. Look for 2/3 or 3/3 alignment in your intended trade direction. Avoid trades when timeframes are mixed or opposing.
Step 2: Verify ADX Strength
Ensure ADX is above 20 (preferably above 30) for trend-following trades. ADX below 20 suggests ranging market where trend strategies underperform.
Step 3: Confirm Donchian Position
Check if price is near or breaking through Donchian channels. Breakouts with strong ADX signal high-momentum moves.
Step 4: Assess VWAP Position
For long trades, prefer price above VWAP. For short trades, prefer price below VWAP. Large distances from VWAP may indicate overextension.
Step 5: Check for Divergences
Look for RSI divergence warnings. If divergence appears with extreme strength score, consider taking profits or tightening stops.
Step 6: Review Strength Score
Use the overall strength score as final filter. Scores above 70 indicate strong trend conditions favorable for trend-following. Scores below 40 suggest caution.
Best Practices
Use on 5-minute to 1-hour timeframes for optimal multi-timeframe analysis
Wait for 2/3 or 3/3 MTF alignment before entering trend trades
Strong ADX (> 30) with MTF alignment produces highest-probability setups
Donchian breakouts with ADX > 25 often lead to sustained moves
VWAP acts as dynamic support/resistance - use for entry refinement
RSI divergences in strong trends often lead to pullbacks, not reversals
Strength score above 80 suggests strong trend continuation potential
Avoid trading when strength score is below 40 unless counter-trend trading
Combine with price action and key levels for precise entries
Indicator Limitations
ADX is lagging indicator - trend strength confirmed after move has started
Donchian breakouts can produce false signals in choppy markets
VWAP resets daily, may not reflect longer-term institutional positioning
Multi-timeframe analysis requires sufficient data history
Strength score is mathematical calculation, not prediction of future movement
Strong trends can reverse suddenly despite high strength scores
Divergences can persist for extended periods in strong trends
Higher timeframe data may repaint on lower timeframes
Requires understanding of trend analysis concepts for effective use
Technical Implementation
Built with Pine Script v6 using:
DMI/ADX calculation with directional indicators
Donchian Channel calculation with breakout detection
VWAP calculation with session reset options
Pivot-based RSI divergence detection
request.security() for multi-timeframe EMA analysis
Comprehensive strength scoring algorithm
Dynamic dashboard with component breakdown
Background coloring based on strength levels
The code is fully open-source and can be modified to adjust timeframes, thresholds, and scoring weights.
Originality Statement
This indicator is original in its multi-timeframe strength integration approach. While individual components (ADX, Donchian Channels, VWAP, RSI divergence, EMAs) are established tools, this mashup is justified because:
It combines trend strength measurement with multi-timeframe directional confirmation
The strength scoring system quantifies trend quality across multiple dimensions
Multi-timeframe analysis prevents counter-trend trades on lower timeframes
Integration of volume-weighted analysis (VWAP) with momentum indicators
Divergence detection provides early warning within trend strength context
Comprehensive dashboard presents complex multi-timeframe data clearly
Each component contributes unique information: ADX measures trend strength, Donchian identifies breakout momentum, VWAP shows institutional positioning, RSI divergences warn of exhaustion, and MTF EMAs ensure alignment. The mashup's value lies in filtering out weak, counter-trend setups and highlighting only those with genuine multi-timeframe strength confirmation.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Trend strength indicators are lagging tools that confirm trends after they've begun. Strong trends can reverse suddenly, and high strength scores do not guarantee trend continuation. Multi-timeframe analysis does not eliminate the risk of losses.
The strength score is a mathematical calculation based on current market data, not a prediction of future price movement. Past trend strength does not guarantee future performance. Market conditions change, and trends that appear strong can reverse without warning.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Harmonic Confluence Wave Detector [JOAT]Harmonic Confluence Wave Detector
Introduction
The Harmonic Confluence Wave Detector is an open-source oscillator-based indicator that combines WaveTrend, Money Flow Index (MFI), RSI, MACD, and Stochastic RSI into a unified momentum analysis system. This mashup creates a multi-layered oscillator framework designed to identify momentum shifts, overbought/oversold conditions, and divergence patterns across multiple timeframes and calculation methods.
The indicator addresses a common trading challenge: single oscillators can give conflicting or premature signals. By synthesizing five different momentum calculations that use distinct mathematical approaches, this tool provides confluence-based signals that occur when multiple momentum indicators align, significantly reducing false signals compared to using any single oscillator alone.
Chart showing WaveTrend oscillator, MACD histogram, and multi-signal system on 1H timeframe
Why This Mashup Exists
This indicator combines five oscillators that complement each other through different calculation methodologies:
WaveTrend: Smoothed momentum oscillator based on price deviation from exponential moving average
Money Flow Index (MFI): Volume-weighted RSI showing buying/selling pressure
RSI: Classic momentum oscillator measuring speed and magnitude of price changes
MACD: Trend-following momentum indicator showing relationship between two EMAs
Stochastic RSI: Stochastic calculation applied to RSI for enhanced sensitivity
Each oscillator has unique strengths: WaveTrend excels at identifying wave-like momentum cycles, MFI incorporates volume for institutional flow analysis, RSI provides reliable overbought/oversold readings, MACD shows trend strength and direction, and Stochastic RSI catches early momentum shifts. Together, they create a comprehensive momentum picture that no single oscillator can provide.
The mashup is justified because these oscillators use fundamentally different calculations (price-based, volume-weighted, moving average convergence, stochastic) that respond to different market conditions. When they align, it indicates genuine momentum shift rather than noise.
Core Components Explained
1. WaveTrend Oscillator (Primary Signal Generator)
WaveTrend is the primary oscillator, calculated using this methodology:
// Calculate exponential average of HLC3
esa = ta.ema(hlc3, channelLength)
// Calculate deviation
d = ta.ema(abs(hlc3 - esa), channelLength)
// Calculate channel index
ci = (hlc3 - esa) / (0.015 * d)
// Apply smoothing to create WaveTrend 1
wt1 = ta.ema(ci, averageLength)
// Create WaveTrend 2 as simple moving average of WT1
wt2 = ta.sma(wt1, 4)
WaveTrend oscillates around zero, with:
Values above +60: Overbought zone
Values above +80: Extreme overbought
Values below -60: Oversold zone
Values below -80: Extreme oversold
Crossovers between WT1 and WT2: Momentum shift signals
The indicator plots WT1 and WT2 as lines with dynamic coloring based on momentum direction and strength.
2. Money Flow Index (MFI) - Volume-Weighted Momentum
MFI calculation incorporates both price and volume:
// Calculate typical price
typicalPrice = (high + low + close) / 3
// Calculate raw money flow
rawMoneyFlow = typicalPrice * volume
// Separate positive and negative money flow
positiveFlow = close > close ? rawMoneyFlow : 0
negativeFlow = close < close ? rawMoneyFlow : 0
// Sum over MFI period
positiveSum = sum(positiveFlow, mfiLength)
negativeSum = sum(negativeFlow, mfiLength)
// Calculate MFI
mfi = 100 - (100 / (1 + positiveSum / negativeSum))
MFI ranges from 0-100, with readings above 80 indicating buying pressure and below 20 indicating selling pressure. The indicator plots MFI as a line and uses it for confluence scoring.
3. RSI - Classic Momentum Oscillator
Standard RSI calculation over 14 periods (configurable):
RSI > 70: Overbought
RSI < 30: Oversold
RSI > 65 with other bearish signals: Potential reversal
RSI < 35 with other bullish signals: Potential reversal
RSI provides reliable baseline momentum readings and is used for divergence detection.
4. MACD - Trend Momentum Indicator
MACD uses standard 12/26/9 settings:
= ta.macd(close, 12, 26, 9)
The indicator displays MACD histogram with enhanced width (linewidth 8) for visibility. Histogram color changes based on:
Green: Positive and increasing (bullish momentum)
Light green: Positive but decreasing (weakening bulls)
Red: Negative and decreasing (bearish momentum)
Light red: Negative but increasing (weakening bears)
MACD histogram provides visual confirmation of momentum strength and direction.
5. Stochastic RSI - Enhanced Sensitivity
Stochastic calculation applied to RSI values:
stochRSI = ta.stoch(rsi, rsi, rsi, 14)
Stochastic RSI oscillates between 0-100 and is more sensitive than regular RSI, catching momentum shifts earlier. The indicator plots both K and D lines for crossover analysis.
Example showing all oscillators with divergence markers and signal labels
Multi-Signal System
The indicator generates six tiers of signals based on confluence strength:
BUY Signals:
BUY: WT1 crosses above WT2 in oversold zone (WT1 < -40)
STRONG BUY: BUY + volume above average + MACD histogram positive
MEGA BUY: STRONG BUY + WT1 < -60 (extreme oversold) + RSI < 35
ULTRA BUY: MEGA BUY + MFI < 30 + Stoch RSI oversold + bullish divergence
SELL Signals:
SELL: WT1 crosses below WT2 in overbought zone (WT1 > 40)
STRONG SELL: SELL + volume above average + MACD histogram negative
MEGA SELL: STRONG SELL + WT1 > 60 (extreme overbought) + RSI > 65
ULTRA SELL: MEGA SELL + MFI > 70 + Stoch RSI overbought + bearish divergence
Signal labels appear on chart with size proportional to signal strength (tiny for BUY/SELL, normal for ULTRA).
Divergence Detection System
The indicator detects divergences across multiple oscillators:
RSI Divergence:
Bullish: Price makes lower low, RSI makes higher low
Bearish: Price makes higher high, RSI makes lower high
WaveTrend Divergence:
Bullish: Price makes lower low, WT1 makes higher low
Bearish: Price makes higher high, WT1 makes lower high
MACD Divergence:
Bullish: Price makes lower low, MACD histogram makes higher low
Bearish: Price makes higher high, MACD histogram makes lower high
Divergences are marked with bright orange/yellow "D" labels (color.rgb(255, 200, 0)) with black text for maximum visibility. When multiple oscillators show divergence simultaneously, it signals strong momentum exhaustion and potential reversal.
Confluence Scoring System
The indicator calculates a real-time confluence score (0-100) by evaluating:
Confluence Components:
- WaveTrend Position: Up to 25 points (extreme zones add more weight)
- WaveTrend Momentum: Up to 15 points (WT1-WT2 relationship)
- RSI Level: Up to 15 points (extreme readings add weight)
- MFI Level: Up to 15 points (volume pressure confirmation)
- MACD Histogram: Up to 15 points (trend momentum)
- Stochastic RSI: Up to 10 points (early momentum detection)
- Divergence Presence: Up to 5 points (any divergence detected)
The dashboard displays the current confluence score with color coding:
Green (80-100): Strong bullish confluence
Light green (60-79): Moderate bullish confluence
Yellow (40-59): Neutral/mixed signals
Light red (20-39): Moderate bearish confluence
Red (0-19): Strong bearish confluence
Visual Elements
WaveTrend Lines: WT1 (blue) and WT2 (orange) with dynamic coloring
Overbought/Oversold Zones: Horizontal lines at +60/-60 and +80/-80
Zero Line: Reference line at 0
MACD Histogram: Large bars (linewidth 8) with gradient coloring
MFI Line: Purple line showing volume-weighted momentum
RSI Line: Green line with overbought/oversold reference levels
Stochastic RSI: K (blue) and D (red) lines
Signal Labels: BUY/SELL markers with size based on signal strength
Divergence Labels: Bright orange "D" markers at divergence points
Dashboard: Top-right table showing confluence score and oscillator readings
Chart demonstrating signal hierarchy from BUY to ULTRA BUY with divergence markers
How Components Work Together
The mashup creates a layered momentum analysis:
Layer 1 - Primary Momentum: WaveTrend identifies wave cycles and crossover signals
Layer 2 - Volume Confirmation: MFI validates moves with volume-weighted pressure
Layer 3 - Baseline Momentum: RSI provides reliable overbought/oversold context
Layer 4 - Trend Strength: MACD histogram shows underlying trend momentum
Layer 5 - Early Detection: Stochastic RSI catches momentum shifts before other oscillators
Layer 6 - Exhaustion Signals: Divergences across oscillators indicate momentum exhaustion
Example scenario: WT1 crosses above WT2 in oversold zone (Layer 1), MFI shows buying pressure increasing (Layer 2), RSI is below 35 (Layer 3), MACD histogram turns positive (Layer 4), Stochastic RSI crosses up (Layer 5), and RSI shows bullish divergence (Layer 6). This generates an ULTRA BUY signal with 90+ confluence score.
Input Parameters
WaveTrend Settings:
Channel Length: Period for EMA calculation (default: 10)
Average Length: Smoothing period for WT1 (default: 21)
Overbought Level: Upper threshold (default: 60)
Oversold Level: Lower threshold (default: -60)
Extreme OB Level: Extreme upper threshold (default: 80)
Extreme OS Level: Extreme lower threshold (default: -80)
Oscillator Settings:
RSI Length: Period for RSI calculation (default: 14)
MFI Length: Period for MFI calculation (default: 14)
MACD Fast: Fast EMA period (default: 12)
MACD Slow: Slow EMA period (default: 26)
MACD Signal: Signal line period (default: 9)
Stochastic RSI Length: Period for Stoch RSI (default: 14)
Signal Settings:
Show Signals: Toggle signal labels (default: enabled)
Show Divergences: Toggle divergence markers (default: enabled)
Volume Confirmation: Require volume for STRONG signals (default: enabled)
Min Confluence for Signals: Minimum score to display signals (default: 60)
Display Options:
Show Dashboard: Toggle confluence score table (default: enabled)
Show MACD Histogram: Toggle MACD display (default: enabled)
Show MFI Line: Toggle MFI display (default: enabled)
Show RSI Line: Toggle RSI display (default: enabled)
Show Stochastic RSI: Toggle Stoch RSI display (default: enabled)
Color Theme: Choose between multiple color schemes
How to Use This Indicator
Step 1: Monitor WaveTrend Oscillator
Watch for WT1/WT2 crossovers in extreme zones. Crossovers in oversold zone (< -60) suggest bullish reversals, crossovers in overbought zone (> 60) suggest bearish reversals.
Step 2: Check Confluence Score
Review the dashboard. Scores above 70 indicate strong momentum alignment. Higher scores generally produce more reliable signals.
Step 3: Identify Signal Strength
Pay attention to signal labels. ULTRA signals have highest probability but occur less frequently. STRONG signals offer good balance between frequency and reliability.
Step 4: Look for Divergences
Divergence markers indicate momentum exhaustion. When divergences appear with extreme oscillator readings, reversal probability increases significantly.
Step 5: Confirm with MACD Histogram
Check MACD histogram direction and strength. Large histogram bars confirm strong momentum, shrinking bars suggest momentum loss.
Step 6: Validate with Volume (MFI)
Ensure MFI supports the move. Bullish signals with rising MFI are stronger, bearish signals with falling MFI are stronger.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal signal quality
Wait for STRONG or MEGA signals rather than acting on every BUY/SELL
Divergences work best when combined with extreme oscillator readings
Multiple oscillator divergences (RSI + WT + MACD) are most reliable
Use confluence score as filter - avoid signals below 60 score
MACD histogram size indicates momentum strength - larger bars = stronger moves
MFI divergence from price often precedes reversals (volume leads price)
Combine with price action and support/resistance for best results
Indicator Limitations
Oscillators can remain overbought/oversold longer than expected in strong trends
Divergences can persist for multiple bars before reversal occurs
Multiple signals in choppy markets can lead to whipsaws
Confluence score is mathematical calculation, not prediction of future movement
ULTRA signals are rare - waiting only for these may miss opportunities
Volume data quality varies across markets and can affect MFI reliability
Stochastic RSI is very sensitive and can generate premature signals
No indicator combination eliminates false signals entirely
Requires understanding of oscillator behavior for effective interpretation
Technical Implementation
Built with Pine Script v6 using:
Custom WaveTrend calculation with dual-line system
Proper MFI formula with volume-weighted money flow
Multi-oscillator divergence detection with pivot analysis
Confluence scoring algorithm with weighted components
Enhanced MACD histogram visualization (linewidth 8)
Dynamic color gradients for momentum visualization
Anti-overlap logic for signal labels
Real-time dashboard with oscillator readings
The code is fully open-source and can be modified to adjust oscillator weights, signal thresholds, and visual preferences.
Originality Statement
This indicator is original in its multi-oscillator integration approach. While individual components (WaveTrend, MFI, RSI, MACD, Stochastic RSI) are established oscillators, this mashup is justified because:
It combines five oscillators using fundamentally different calculation methods
The tiered signal system (BUY to ULTRA) provides graduated confidence levels
Multi-oscillator divergence detection catches momentum exhaustion across different timeframes
Confluence scoring quantifies momentum alignment across all oscillators
Volume integration through MFI adds institutional flow perspective
Enhanced visualization (large MACD histogram, bright divergence markers) improves usability
Each oscillator contributes unique information: WaveTrend provides wave-cycle analysis, MFI incorporates volume, RSI offers reliable baseline, MACD shows trend strength, and Stochastic RSI catches early shifts. The mashup's value lies in identifying when these different momentum calculations align, significantly reducing false signals compared to any single oscillator.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Oscillator-based indicators are lagging tools that analyze past price data. They do not predict future price movement. Overbought conditions can persist in strong uptrends, and oversold conditions can persist in strong downtrends. Divergences can continue for extended periods before reversals occur.
The confluence score is a mathematical calculation, not a guarantee of trade success. High confluence scores do not ensure profitable trades. Past signal performance does not guarantee future results. Market conditions change, and oscillator behavior varies across different market regimes.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Precision Pivot Confluence Engine [JOAT]Precision Pivot Confluence Engine
Introduction
The Precision Pivot Confluence Engine is an open-source technical indicator that combines Central Pivot Range (CPR) analysis with Smart Money Concepts (SMC), multi-oscillator divergence detection, and institutional order flow patterns. This mashup integrates multiple proven methodologies into a unified confluence system designed to identify high-probability trading zones where institutional and retail liquidity intersect.
The indicator is built for traders who understand that no single signal provides consistent edge, but multiple confirming factors working together can significantly improve trade selection. By synthesizing CPR levels, Fair Value Gaps, Order Blocks, liquidity sweeps, and divergence patterns, this tool helps identify structural market inflection points.
Chart showing CPR levels, FVG zones, Order Blocks, and divergence signals on 4H timeframe
Why This Mashup Exists
This indicator combines five distinct analytical frameworks that complement each other:
CPR Analysis: Identifies key pivot levels where institutional algorithms and retail traders make decisions
Smart Money Concepts: Tracks Fair Value Gaps, Order Blocks, and Breaker Blocks showing institutional positioning
Divergence Detection: Uses RSI, MACD, and Stochastic RSI to identify momentum exhaustion
Liquidity Analysis: Detects liquidity sweeps where stop hunts occur before reversals
Volume Confirmation: Validates moves with volume analysis and delta calculations
Each component addresses a different aspect of market structure. CPR provides static reference levels, SMC reveals dynamic institutional behavior, divergences show momentum shifts, liquidity sweeps identify stop hunts, and volume confirms genuine moves versus noise. Together, they create a multi-dimensional view of market conditions.
Core Components Explained
1. Enhanced CPR System
The Central Pivot Range system calculates Daily and Weekly pivot levels using the formula:
Pivot = (High + Low + Close) / 3
BC (Bottom Central) = (High + Low) / 2
TC (Top Central) = (Pivot - BC) + Pivot
The indicator analyzes CPR width to determine market regime:
Narrow CPR (width < 0.5%): Indicates compression and potential breakout conditions
Wide CPR (width > 1.5%): Suggests ranging market with less directional conviction
Price position relative to CPR: Above both Daily and Weekly pivots = bullish structure, below = bearish structure
CPR levels act as magnetic zones where price tends to react. The indicator tracks distance from pivots to identify overextension and mean reversion opportunities.
2. Smart Money Concepts Integration
Fair Value Gaps (FVG):
Bullish FVG occurs when current low > high from 2 bars ago, leaving an unfilled gap
Bearish FVG occurs when current high < low from 2 bars ago
The indicator calculates FVG size as percentage of price and filters for significant gaps (> 0.3%) to avoid noise. FVGs represent inefficient price delivery where institutions moved price quickly, often returning to fill these gaps later.
Order Blocks (OB):
Bullish OB: Two consecutive bearish candles followed by strong bullish candle with high volume
Bearish OB: Two consecutive bullish candles followed by strong bearish candle with high volume
Order Blocks mark the last opposite-direction move before a strong impulse, indicating where institutions accumulated or distributed positions.
Breaker Blocks:
Failed Order Blocks that get violated become Breaker Blocks, signaling potential trend reversal. The indicator tracks the last bullish and bearish OB levels and alerts when price breaks through them.
Liquidity Sweeps:
The indicator identifies when price briefly exceeds recent highs/lows (20-bar lookback) but closes back inside the range. These "stop hunts" often precede reversals as institutions trigger retail stops before moving price in the intended direction.
Example showing FVG zones, Order Blocks, and liquidity sweep markers
3. Multi-Oscillator Divergence System
The indicator simultaneously tracks divergences across three oscillators:
RSI Divergence:
Bullish: Price makes lower low, RSI makes higher low (momentum improving despite price weakness)
Bearish: Price makes higher high, RSI makes lower high (momentum deteriorating despite price strength)
MACD Divergence:
Tracks histogram divergences using the same pivot-based logic
Stochastic RSI Divergence:
More sensitive than RSI, catches early momentum shifts
The indicator uses a 5-bar pivot lookback to identify swing highs/lows and compares current pivots with previous pivots to detect divergences. When multiple oscillators show divergence simultaneously, it signals strong momentum exhaustion.
4. Volume Analysis Engine
Volume MA Comparison: Identifies high volume (> 1.5x MA) and climax volume (> 3x MA)
Volume Delta: Cumulative difference between buying volume (green candles) and selling volume (red candles)
Delta Trend: Compares current delta to 20-period MA to identify institutional accumulation or distribution
Volume Confirmation: Validates bullish moves with high volume + rising delta, bearish moves with high volume + falling delta
5. Confluence Scoring System
The indicator calculates a real-time confluence score (0-100) by weighting each component:
Confluence Score Components:
- CPR Position: Up to 15 points (bullish above pivots, bearish below)
- SMC Signals: Up to 10 points (FVG + OB + Breaker + Liquidity Sweeps)
- Divergence: Up to 10 points (single oscillator = 5, multiple = 10)
- Volume: Up to 10 points (confirmed volume = 7, climax = additional 3)
- Trend Alignment: Up to 5 points (price vs key MAs)
Scores above 70 indicate strong confluence for potential trades. The dashboard displays individual component scores for transparency.
Visual Elements
CPR Lines: Daily Pivot (yellow), TC/BC (yellow transparent), Weekly Pivot (yellow circles)
FVG Boxes: Green boxes for bullish FVGs, red boxes for bearish FVGs
Order Block Boxes: Solid green/red boxes marking institutional zones
Breaker Block Labels: "BB" markers when Order Blocks fail
Liquidity Sweep Labels: "LIQ" and "STRONG LIQ" positioned at sweep tips
Divergence Labels: "D" markers at divergence pivot points
Dashboard: Top-right table showing confluence score and component breakdown
How Components Work Together
The mashup creates a layered analysis approach:
Layer 1 - Structure: CPR levels define key zones where reactions are likely
Layer 2 - Institutional Behavior: SMC concepts show where smart money is positioned
Layer 3 - Momentum: Divergences indicate when current trend is losing steam
Layer 4 - Confirmation: Volume validates whether moves are genuine or false
Layer 5 - Synthesis: Confluence score combines all factors into actionable signal
Example scenario: Price approaches Daily Pivot (Layer 1) where a bullish Order Block exists (Layer 2), RSI shows bullish divergence (Layer 3), and volume delta is rising (Layer 4). The confluence score jumps to 85 (Layer 5), signaling high-probability long setup.
Input Parameters
CPR Settings:
Show Daily CPR: Toggle daily pivot levels (default: enabled)
Show Weekly CPR: Toggle weekly pivot levels (default: enabled)
CPR Width Threshold: Defines narrow vs wide CPR (default: 0.5% / 1.5%)
Smart Money Concepts:
Show FVG: Display Fair Value Gap boxes (default: enabled)
Show Order Blocks: Display Order Block boxes (default: enabled)
Show Breaker Blocks: Display Breaker Block labels (default: enabled)
Show Liquidity Sweeps: Display liquidity sweep markers (default: enabled)
FVG Min Size: Minimum gap size to display (default: 0.3%)
Lookback Bars: Bars to scan for liquidity levels (default: 20)
Divergence Detection:
Show Divergences: Toggle divergence labels (default: enabled)
RSI Length: Period for RSI calculation (default: 14)
Pivot Lookback: Bars for pivot detection (default: 5)
Volume Analysis:
Show Volume Analysis: Toggle volume indicators (default: enabled)
Volume MA Length: Period for volume moving average (default: 20)
High Volume Multiplier: Threshold for high volume (default: 1.5x)
Climax Volume Multiplier: Threshold for climax volume (default: 3.0x)
Display Options:
Show Dashboard: Toggle confluence score table (default: enabled)
Max FVG Boxes: Limit displayed FVG boxes (default: 20)
Max OB Boxes: Limit displayed Order Block boxes (default: 15)
Label Spacing: Minimum bars between labels to prevent overlap (default: 10-15)
How to Use This Indicator
Step 1: Identify Market Structure
Check CPR position and width. Narrow CPR suggests breakout potential, wide CPR suggests range-bound conditions.
Step 2: Look for SMC Confluence
Identify FVGs, Order Blocks, and recent liquidity sweeps. These zones often provide high-probability entry areas.
Step 3: Check for Divergences
Look for divergence labels at swing points. Multiple oscillator divergences increase signal strength.
Step 4: Confirm with Volume
Ensure volume supports the move. Rising delta + high volume confirms bullish moves, falling delta + high volume confirms bearish moves.
Step 5: Review Confluence Score
Check the dashboard. Scores above 70 indicate strong confluence. Individual component scores show which factors are contributing.
Step 6: Wait for Price Action Confirmation
The indicator identifies zones and conditions, but wait for price action confirmation (candlestick patterns, breakouts, etc.) before entering trades.
Best Practices
Use on 15-minute to 4-hour timeframes for optimal signal quality
Combine with proper risk management - indicator shows zones, not exact entries
Pay attention to confluence score - higher scores generally indicate better setups
Watch for FVG fills and Order Block retests as entry triggers
Liquidity sweeps followed by reversal often provide excellent risk:reward entries
Divergences work best when combined with SMC zones or CPR levels
Volume confirmation is critical - avoid low-volume signals
Indicator Limitations
Does not provide exact entry/exit signals - requires trader interpretation
Can generate false signals in choppy, low-volume conditions
Multiple visual elements may clutter chart - adjust display settings as needed
Divergences can persist longer than expected - price can continue trending despite divergence
FVGs and Order Blocks don't always get retested - not every zone provides entry opportunity
Confluence score is a guide, not a guarantee - high scores can still result in losing trades
Requires understanding of SMC concepts and CPR analysis for effective use
Performance varies across different markets and timeframes
Technical Implementation
Built with Pine Script v6 using:
Custom CPR calculations with width analysis
Box and label management with anti-overlap logic
Persistent variables for tracking Order Blocks and Breaker Blocks
Pivot-based divergence detection across multiple oscillators
Volume delta calculation with cumulative tracking
Real-time confluence scoring system
Dynamic dashboard with component breakdown
The code is fully open-source and can be modified to suit individual trading styles and preferences.
Originality Statement
This indicator is original in its integration approach. While individual components (CPR, FVG, Order Blocks, RSI divergence, volume analysis) are established concepts, this mashup is justified because:
It synthesizes five distinct methodologies that address different market aspects
The confluence scoring system provides quantitative measurement of setup quality
Anti-overlap logic and timeframe-adaptive filtering reduce visual clutter
Component integration creates layered analysis not available in individual indicators
The combination helps identify zones where multiple institutional and technical factors align
Each component contributes unique information: CPR provides static structure, SMC reveals dynamic institutional behavior, divergences show momentum shifts, liquidity analysis identifies stop hunts, and volume confirms genuine moves. The mashup's value lies in presenting these complementary perspectives simultaneously with a unified scoring system.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss and is not suitable for all investors.
Technical indicators are tools for analysis, not crystal balls. Past performance and backtested results do not guarantee future performance. Market conditions change, and strategies that worked historically may not work in the future.
The confluence score is a mathematical calculation based on current market data, not a prediction of future price movement. High confluence scores do not guarantee profitable trades. Users must conduct their own analysis and risk assessment before making trading decisions.
Always use proper risk management, including stop losses and position sizing appropriate for your account size and risk tolerance. Never risk more than you can afford to lose. Consider consulting with a qualified financial advisor before making investment decisions.
The author is not responsible for any losses incurred from using this indicator. Users assume full responsibility for all trading decisions made using this tool.
-Made with passion by officialjackofalltrades Indicator
Institutional Confluence Mapper [JOAT]Institutional Confluence Mapper (ICM)
Introduction
The Institutional Confluence Mapper is an open-source multi-factor analysis tool that combines five analytical modules into a unified confluence scoring system. It synthesizes institutional trading concepts including Relative Rotation analysis, Smart Money flow detection, Liquidity zone mapping, Session-based timing, and Volatility regime classification.
Rather than relying on a single indicator, ICM evaluates market conditions through multiple lenses simultaneously, presenting a clear confluence score (0-100%) that reflects the alignment of various market factors.
This script is fully open-source under the Mozilla Public License 2.0.
Originality and Purpose
This indicator is NOT a random mashup of existing indicators. It is an original implementation that creates a unified institutional analysis framework:
Why Multiple Modules? Most retail traders struggle because they rely on single indicators that provide conflicting signals. Institutional traders evaluate markets through multiple frameworks simultaneously. ICM bridges this gap by providing a unified view of complementary analysis methods.
The Confluence Scoring System: Each module contributes to a weighted confluence score (0-100%). Scores above 65% indicate bullish confluence; below 35% indicates bearish confluence.
How Components Work Together:
RRG (Relative Rotation) determines macro bias - is this asset outperforming or underperforming its benchmark?
Institutional Flow confirms smart money activity - are institutions accumulating or distributing?
Volatility Regime determines strategy selection - trend-follow or mean-revert?
Liquidity Detection identifies key levels - where are the stop hunts happening?
Session Analysis optimizes timing - when should you trade?
The Five Core Modules
1. Relative Rotation Momentum Matrix (RRG)
Compares the current symbol against a benchmark (default: SPY) using the JdK RS-Ratio methodology with double-smoothed EMA. Assets rotate through four quadrants:
LEADING: Outperforming with positive momentum (strongest bullish)
WEAKENING: Outperforming but losing momentum
LAGGING: Underperforming with negative momentum (strongest bearish)
IMPROVING: Underperforming but gaining momentum
2. Institutional Flow Analysis
Analyzes volume patterns to detect smart money activity:
Volume Z-Score measures how unusual current volume is
Buy/Sell pressure estimation based on candle structure
Unusual volume detection highlights institutional activity
3. Volatility Regime System
Uses ATR percentile ranking to classify market conditions:
COMPRESSION: Low volatility (ATR < 20th percentile) - potential breakout
EXPANSION: High volatility (ATR > 80th percentile) - trending
TRENDING_BULL/BEAR: Directional trends based on EMA alignment
RANGING: Sideways consolidation
4. Liquidity Detection
Identifies institutional liquidity targets using swing point analysis:
Swing highs/lows are tracked and displayed as dashed lines
Purple dashed lines mark resistance/sell-side liquidity
Teal dashed lines mark support/buy-side liquidity
Gold diamonds appear when liquidity sweeps are detected (potential reversals)
5. Session Momentum Profiler
Tracks trading sessions based on your selected timezone:
Asian Session: 7PM - 4AM EST
London Session: 3AM - 12PM EST
New York Session: 9:30AM - 4PM EST
London/NY Overlap: 8AM - 12PM EST (peak liquidity)
Visual Elements
Main Dashboard (Top-Right):
BIAS: Overall direction with confluence percentage
RRG: Current quadrant and momentum
FLOW: Smart money bias and volume status
REGIME: Market condition and volatility percentile
SESSION: Active trading session and current time
LIQUIDITY: Active zones and grab signals
SIGNAL: Actionable recommendation
Chart Elements:
Gold Diamond: Liquidity grab (potential reversal point)
Teal Dashed Line: Support / Buy-side liquidity zone
Purple Dashed Line: Resistance / Sell-side liquidity zone
EMA 21/55/200: Trend structure with cloud fill
Volatility Bands: ATR-based channels
How to Use
Step 1: Check the BIAS row for overall market direction
Step 2: Check REGIME to understand market conditions
Step 3: Identify key levels using liquidity zones and EMAs
Step 4: Wait for confluence above 65% (bullish) or below 35% (bearish)
Step 5: Look for gold diamond signals at key levels
Best Setups
Bullish: Confluence >65%, RRG in LEADING/IMPROVING, bullish flow, price near teal support zone.
Bearish: Confluence <35%, RRG in LAGGING/WEAKENING, bearish flow, price near purple resistance zone.
Reversal: Gold diamond appears after price sweeps a liquidity zone.
Key Input Parameters
Benchmark Symbol: Compare against (default: SPY)
RS-Ratio/Momentum Lookback: RRG calculation periods
Volume Analysis Period: Flow detection lookback
Swing Length: Liquidity zone detection
ATR Period/Rank Period: Regime classification
Timezone: Session detection timezone
Alerts
Liquidity Grab Bull: Bullish sweep detected
Liquidity Grab Bear: Bearish sweep detected
High Confluence Bull: Confluence above 70%
High Confluence Bear: Confluence below 30%
Best Practices
Use on 1H, 4H, or Daily timeframes for reliable signals
Combine with price action for confirmation
Respect the regime - don't fight strong trends
Trade during London/NY overlap for best liquidity
Wait for high confluence scores before entering
Always use proper risk management
Limitations
Works best on liquid markets with sufficient volume
Session features optimized for forex/crypto markets
RRG requires a valid benchmark symbol
No indicator predicts the future - use proper risk management
Disclaimer
This indicator is for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results.
-Made with passion by officialjackofalltrades
Indicator
TurboRSI Pro [JOAT]TurboRSI Pro - Multi-Length RSI Ensemble with Dynamic Momentum Analysis
Introduction
TurboRSI Pro is an open-source indicator that reimagines the classic RSI by calculating multiple RSI lengths simultaneously and combining them into a single, more reliable momentum reading. Instead of relying on a single RSI period that may lag or produce false signals, this indicator creates an ensemble of RSI values across a configurable range, providing a smoother and more robust momentum assessment.
The indicator is designed for traders who want deeper insight into momentum conditions without the noise that comes from single-period oscillators.
Originality and Purpose
This indicator is NOT a simple RSI with different settings. It is an original implementation that solves a fundamental problem with traditional RSI:
The Problem with Single-Period RSI: Traditional RSI uses a single lookback period (typically 14). The issue is that different market conditions favor different RSI lengths. A 14-period RSI might work well in one market phase but produce false signals in another. There's no "perfect" RSI length that works in all conditions.
The Multi-Length Solution: TurboRSI Pro calculates RSI across a range of lengths (default: 10 to 20) simultaneously, then averages all values to create a composite reading. This ensemble approach filters out period-specific noise while preserving genuine momentum shifts. When multiple RSI lengths agree, the signal is more reliable.
OB/OS Strength Percentage: The indicator tracks how many individual RSI lengths are in overbought or oversold territory. When 100% of lengths are overbought, it's a much stronger signal than when only 50% are. This percentage-based approach is original to this indicator and provides conviction assessment.
Candle Heatmap Innovation: An optional feature colors price bars based on deviation from a 200-bar linear regression line. This shows when price is statistically overextended (HOT/COLD) independent of RSI, providing another layer of analysis.
How the components work together:
Multi-length RSI ensemble provides a more robust momentum reading than single-period RSI
OB/OS Strength percentages quantify how many timeframes agree on the momentum condition
Dynamic channels expand/contract based on momentum strength across all calculated lengths
Candle heatmap adds statistical price deviation context independent of RSI
Core Concept: Multi-Length RSI Ensemble
Traditional RSI uses a single lookback period (typically 14). The problem is that different market conditions favor different RSI lengths. TurboRSI Pro solves this by:
Calculating RSI across a range of lengths (default: 10 to 20)
Averaging all RSI values to create a composite reading
Tracking how many individual RSI lengths are in overbought or oversold territory
Displaying this information as "OB Strength" and "OS Strength" percentages
This approach filters out noise while preserving genuine momentum shifts.
How the Multi-Length RSI Works
The calculation uses an efficient array-based approach:
int N = maxLength - minLength + 1
float diff = nz(srcInput - srcInput )
for i = 0 to N - 1
int len = minLength + i
float alpha = 1.0 / len
float numRma = alpha * diff + (1 - alpha) * array.get(numArr, i)
float denRma = alpha * math.abs(diff) + (1 - alpha) * array.get(denArr, i)
float rsiVal = denRma != 0 ? 50 * numRma / denRma + 50 : 50
avgRSI += rsiVal
Each RSI length is calculated using the RMA (Running Moving Average) formula, then all values are averaged. The result is a composite RSI that responds to momentum changes while filtering out period-specific noise.
Visual Components
1. Multi-Length RSI Line
The main oscillator line displays the averaged RSI value with a gradient color:
Green gradient when RSI is above 50 (bullish momentum)
Red gradient when RSI is below 50 (bearish momentum)
Color intensity increases as RSI approaches extreme levels
2. Dynamic Channels
Two adaptive channel lines track momentum extremes:
Upper Channel: Expands when multiple RSI lengths enter overbought territory
Lower Channel: Expands when multiple RSI lengths enter oversold territory
Channel width indicates momentum strength across all calculated lengths
3. Candle Heatmap
An optional feature that colors price bars based on deviation from a linear regression line:
Red/Orange bars: Price is significantly above the regression line (overextended to upside)
Blue bars: Price is significantly below the regression line (overextended to downside)
Yellow bars: Price is near the regression line (neutral)
The heatmap uses a 200-bar regression calculation to identify when price has deviated significantly from its statistical trend.
4. Reference Lines
Standard RSI reference levels are displayed:
80 and 20: Extreme overbought/oversold
70 and 30: Standard overbought/oversold thresholds
50: Neutral momentum line
5. Background Zones
Shaded areas indicate the percentage of RSI lengths in extreme territory:
Green shading from bottom: Percentage of lengths in overbought
Red shading from top: Percentage of lengths in oversold
Dashboard Panel
The dashboard displays real-time analysis in a 7-row table:
RSI Value: Current composite RSI reading (large text for visibility)
Momentum: Current state - OVERBOUGHT, OVERSOLD, BULLISH, BEARISH, or NEUTRAL
OB Strength: Percentage of RSI lengths currently above the overbought threshold
OS Strength: Percentage of RSI lengths currently below the oversold threshold
Heat Level: Current price deviation state - HOT, WARM, NEUTRAL, COOL, or COLD
Trend Bias: Overall trend assessment based on RSI level and channel direction
Optional Stochastic RSI
When enabled, an additional Stochastic RSI line is plotted. This applies the stochastic formula to the RSI itself, providing another layer of momentum analysis. The Stochastic RSI is more sensitive to short-term momentum shifts.
Input Parameters
RSI Settings:
Min RSI Length: Starting length for the RSI range (default: 10)
Max RSI Length: Ending length for the RSI range (default: 20)
Source: Price source for calculation (default: ohlc4)
Overbought: Upper threshold (default: 70)
Oversold: Lower threshold (default: 30)
Candle Heatmap:
Enable Heatmap: Toggle bar coloring on/off (default: enabled)
Regression Length: Lookback for linear regression calculation (default: 200)
Display:
Show Dashboard: Toggle the information panel (default: enabled)
Show Dynamic Channels: Toggle channel lines (default: enabled)
Show Stochastic RSI: Toggle additional Stoch RSI line (default: disabled)
Colors:
Bullish: Color for bullish conditions (default: teal)
Bearish: Color for bearish conditions (default: red)
Neutral: Color for neutral conditions (default: gray)
How to Use TurboRSI Pro
Identifying Momentum Shifts:
Watch for RSI crossing above 50 for bullish momentum confirmation
Watch for RSI crossing below 50 for bearish momentum confirmation
Use the gradient color to quickly assess momentum direction
Using OB/OS Strength:
When OB Strength reaches 100%, all RSI lengths are overbought - strong reversal potential
When OS Strength reaches 100%, all RSI lengths are oversold - strong bounce potential
Partial readings (e.g., 50%) indicate mixed conditions across timeframes
Heatmap Analysis:
HOT readings combined with high RSI suggest overextension - caution for longs
COLD readings combined with low RSI suggest oversold conditions - watch for reversal
Use heatmap divergence from RSI for additional confirmation
Channel Interpretation:
Expanding upper channel with rising RSI confirms strong bullish momentum
Expanding lower channel with falling RSI confirms strong bearish momentum
Channel contraction suggests momentum is weakening
Alert Conditions
Six alert conditions are available:
RSI Overbought: RSI crosses above overbought threshold
RSI Oversold: RSI crosses below oversold threshold
RSI Bullish Cross: RSI crosses above 50
RSI Bearish Cross: RSI crosses below 50
All RSI Overbought: Every RSI length is in overbought territory
All RSI Oversold: Every RSI length is in oversold territory
Best Practices
Use on higher timeframes (1H, 4H, Daily) for more reliable signals
Combine with price action analysis - RSI confirms, it does not predict
Pay attention to OB/OS Strength percentages for conviction assessment
The heatmap works best on assets with clear trending behavior
Adjust min/max RSI lengths based on your trading style - wider range for smoother signals
Limitations
Like all oscillators, can remain in overbought/oversold territory during strong trends
The heatmap regression may lag during rapid price movements
Multi-length calculation requires more processing than single RSI
Best suited for swing trading and position trading timeframes
Technical Notes
This indicator is written in Pine Script v6 and uses:
Array-based calculations for efficient multi-length RSI computation
Linear regression for heatmap deviation analysis
Gradient coloring for intuitive visual feedback
State management for dynamic channel calculations
The source code is open and available for review and modification.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management.
-Made with passion by officialjackofalltrades Indicator
RSI Trend Authority [JOAT]RSI Trend Authority - VAR-RSI with OTT Trend Detection System
Introduction
RSI Trend Authority is an open-source overlay indicator that combines Variable Index Dynamic Average (VAR) smoothed RSI with the Optimized Trend Tracker (OTT) to create a complete trend detection and signal generation system. Unlike traditional RSI which oscillates in a separate pane, this indicator scales the RSI to price and overlays it directly on your chart, making trend analysis more intuitive.
The indicator generates clear BUY and SELL signals when the smoothed RSI crosses the OTT trailing stop line, providing actionable entry points with trend confirmation.
Originality and Purpose
This indicator is NOT a simple mashup of RSI and moving averages. It is an original implementation that transforms RSI into a trend-following overlay system:
Why VAR Smoothing? Traditional RSI is noisy and produces many false signals. The Variable Index Dynamic Average (VAR) is an adaptive smoothing algorithm based on the Chande Momentum Oscillator principle. It adjusts its smoothing factor based on market conditions - responding quickly during trends and smoothing out during choppy markets. This creates an RSI that filters noise while preserving genuine momentum shifts.
Why OTT Trailing Stop? The Optimized Trend Tracker (OTT) is a percentage-based trailing stop mechanism that only moves in the direction of the trend. When VAR-RSI crosses above OTT, a bullish trend is confirmed; when it crosses below, a bearish trend is confirmed. This provides clear, actionable signals rather than subjective interpretation.
Price Scaling Innovation: By scaling RSI (0-100) to price using the formula (RSI * close / 50), the indicator overlays directly on the price chart. This allows traders to see how momentum relates to actual price levels, making trend analysis more intuitive than a separate oscillator pane.
ATR Boundaries: Optional volatility-based boundaries show when price is extended relative to its normal range, helping identify potential reversal zones.
How the components work together:
VAR smoothing removes RSI noise while preserving trend information
OTT provides a dynamic trailing stop that generates clear crossover signals
Price scaling allows direct overlay on the chart for intuitive analysis
ATR boundaries add volatility context for profit target estimation
Core Components
1. VAR-RSI (Variable Index Dynamic Average RSI)
The foundation of this indicator is the VAR smoothing algorithm applied to RSI. VAR is an adaptive moving average that adjusts its smoothing factor based on the Chande Momentum Oscillator principle:
f_var_calc(float data, int length) =>
int a = 9
float b = data > nz(data ) ? data - nz(data ) : 0.0
float c = data < nz(data ) ? nz(data ) - data : 0.0
float d = math.sum(b, a)
float e = math.sum(c, a)
float f = nz((d - e) / (d + e))
float g = math.abs(f)
float h = 2.0 / (length + 1)
float x = ta.sma(data, length)
This creates an RSI that:
Responds quickly during trending conditions
Smooths out during choppy, sideways markets
Reduces false signals compared to raw RSI
2. OTT (Optimized Trend Tracker)
The OTT acts as a dynamic trailing stop that follows the VAR-RSI:
In uptrends, OTT trails below the VAR-RSI line
In downtrends, OTT trails above the VAR-RSI line
The OTT Percent parameter controls how closely it follows
When VAR-RSI crosses above OTT, a bullish trend is confirmed. When VAR-RSI crosses below OTT, a bearish trend is confirmed.
3. Price Scaling
The RSI (0-100 scale) is converted to price scale using:
float scaleFactor = close / 50.0
float varRSIScaled = varRSI * scaleFactor
This allows the indicator to overlay directly on price, showing how momentum relates to actual price levels.
Visual Components
VAR-RSI Line (Cyan/Magenta)
The main indicator line with gradient coloring:
Cyan gradient when RSI is above 50 (bullish)
Magenta gradient when RSI is below 50 (bearish)
Line thickness of 3 for clear visibility
OTT Line (Yellow Circles)
The trailing stop line displayed as circles:
Acts as dynamic support in uptrends
Acts as dynamic resistance in downtrends
Crossovers generate trading signals
Trend Fill
The area between VAR-RSI and OTT is filled:
Cyan fill during bullish trends
Magenta fill during bearish trends
Fill transparency allows price visibility
Buy position and LONG on Dashboard with a Uptrend:
ATR Boundaries (Optional)
Dotted lines showing volatility-based price boundaries:
Upper band: Close + (ATR x Multiplier)
Lower band: Close - (ATR x Multiplier)
Color matches current trend direction
Buy/Sell Signals
Clear labels appear at signal points:
BUY label below bar when VAR-RSI crosses above OTT
SELL label above bar when VAR-RSI crosses below OTT
Additional glow circles highlight signal bars
Bar Coloring
Optional feature that colors price bars:
Cyan bars during bullish trend
Magenta bars during bearish trend
Dashboard Panel
The 8-row dashboard provides comprehensive status information:
Signal: Current position - LONG or SHORT (large text)
VAR-RSI: Current smoothed RSI value (large text)
RSI State: OVERBOUGHT, OVERSOLD, BULLISH, or BEARISH
OTT Trend: UPTREND or DOWNTREND based on OTT direction
Bars Since: Number of bars since last signal
Price: Current close price (large text)
OTT Level: Current OTT trailing stop value
Input Parameters
RSI Settings:
RSI Length: Period for RSI calculation (default: 100)
Source: Price source (default: close)
VAR Settings:
VAR Length: Adaptive smoothing period (default: 50)
OTT Settings:
OTT Period: Trailing stop calculation period (default: 30)
OTT Percent: Distance percentage for trailing stop (default: 0.2)
ATR Trend Boundaries:
Show ATR Boundaries: Toggle visibility (default: enabled)
ATR Length: Period for ATR calculation (default: 14)
ATR Multiplier: Distance multiplier (default: 2.0)
Display Options:
Show Buy/Sell Signals: Toggle signal labels (default: enabled)
Show Status Table: Toggle dashboard (default: enabled)
Table Position: Choose corner placement
Color Bars by Trend: Toggle bar coloring (default: enabled)
Color Scheme:
Bullish Color: Main bullish color (default: cyan)
Bearish Color: Main bearish color (default: magenta)
OTT Line: Trailing stop color (default: yellow)
VAR-RSI Line: Main line color (default: teal)
ATR colors for boundaries
How to Use RSI Trend Authority
Signal-Based Trading:
Enter LONG when BUY signal appears (VAR-RSI crosses above OTT)
Enter SHORT when SELL signal appears (VAR-RSI crosses below OTT)
Use the OTT line as a trailing stop reference
Trend Confirmation:
Cyan fill indicates bullish trend - favor long positions
Magenta fill indicates bearish trend - favor short positions
Check RSI State in dashboard for momentum context
Using the Dashboard:
Monitor "Bars Since" to assess signal freshness
Check RSI State for overbought/oversold warnings
Use OTT Level as a reference for stop placement
ATR Boundaries:
Price near upper ATR band in uptrend suggests extension
Price near lower ATR band in downtrend suggests extension
Boundaries help identify potential reversal zones
Parameter Optimization
For Faster Signals:
Decrease RSI Length (try 50-80)
Decrease VAR Length (try 30-40)
Decrease OTT Period (try 15-25)
For Smoother Signals:
Increase RSI Length (try 120-150)
Increase VAR Length (try 60-80)
Increase OTT Period (try 40-50)
For Tighter Stops:
Decrease OTT Percent (try 0.1-0.15)
For Wider Stops:
Increase OTT Percent (try 0.3-0.5)
Alert Conditions
Three alert conditions are available:
Buy Signal: VAR-RSI crosses above OTT
Sell Signal: VAR-RSI crosses below OTT
Trend Change: OTT direction changes
Understanding the OTT Calculation
The OTT uses a percentage-based trailing mechanism:
float farkOTT = mavgOTT * ottPercent * 0.01
float longStopCalc = mavgOTT - farkOTT
float shortStopCalc = mavgOTT + farkOTT
longStop := mavgOTT > nz(longStop ) ? math.max(longStopCalc, nz(longStop )) : longStopCalc
shortStop := mavgOTT < nz(shortStop ) ? math.min(shortStopCalc, nz(shortStop )) : shortStopCalc
This ensures the trailing stop only moves in the direction of the trend, never against it.
Best Practices
Use on 1H timeframe or higher for more reliable signals
Wait for signal confirmation before entering trades
Consider RSI State when evaluating signal quality
Use ATR boundaries for profit target estimation
The longer RSI length (100) provides smoother trend detection
Combine with support/resistance analysis for better entries
Limitations
Signals may lag during rapid price movements due to smoothing
Works best in trending markets; may whipsaw in ranges
The overlay nature means RSI values are scaled, not absolute
Default parameters are optimized for crypto and forex; adjust for other markets
Technical Notes
This indicator is written in Pine Script v6 and uses:
VAR (Variable Index Dynamic Average) for adaptive smoothing
OTT (Optimized Trend Tracker) for trailing stop calculation
ATR for volatility-based boundaries
Gradient coloring for intuitive trend visualization
The source code is open and available for review and modification.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always conduct your own analysis and use proper risk management.
-Made with passion by officialjackofalltrades Indicator
RSI Fibonacci Flow [JOAT]RSI Fibonacci Flow - Advanced Fibonacci Retracement with RSI Confluence
Introduction
RSI Fibonacci Flow is an open-source overlay indicator that combines automatic Fibonacci retracement levels with RSI momentum analysis to identify high-probability trading zones. The indicator automatically detects swing highs and lows, draws Fibonacci levels, and generates confluence signals when RSI conditions align with key Fibonacci zones.
This indicator is designed for traders who use Fibonacci retracements but want additional confirmation from momentum analysis before entering trades.
Originality and Purpose
This indicator is NOT a simple mashup of RSI and Fibonacci tools. It is an original implementation that creates a synergistic relationship between two complementary analysis methods:
Why Combine RSI with Fibonacci? Fibonacci retracements identify WHERE price might reverse, but they don't tell you WHEN. RSI provides the timing component by showing momentum exhaustion. When price reaches the Golden Zone (50%-61.8%) AND RSI shows oversold conditions, the probability of a successful bounce increases significantly.
Original Confluence Scoring System: The indicator calculates a 0-5 confluence score that weights multiple factors: Golden Zone presence (+2), entry zone presence (+1), RSI extreme alignment (+1), RSI divergence (+1), and strong RSI momentum (+1). This scoring system is original to this indicator.
Automatic Pivot Detection: Unlike manual Fibonacci tools, this indicator automatically detects swing highs and lows using a configurable pivot algorithm, then draws Fibonacci levels accordingly. The pivot detection uses a center-bar comparison method that checks if a bar's high/low is the highest/lowest within the specified depth on both sides.
Dynamic Trend Awareness: The indicator determines trend direction based on pivot sequence (last pivot was high or low) and adjusts Fibonacci orientation accordingly. In uptrends, 0% is at swing low; in downtrends, 0% is at swing high.
Each component serves a specific purpose:
Fibonacci levels identify potential reversal zones based on natural price ratios
RSI provides momentum context to filter out low-probability setups
Confluence scoring quantifies setup quality for position sizing decisions
Automatic pivot detection removes subjectivity from level placement
Core Concept: RSI-Fibonacci Confluence
The most powerful trading setups occur when multiple factors align. RSI Fibonacci Flow identifies these moments by:
Automatically detecting price pivots and drawing Fibonacci levels
Tracking which Fibonacci zone the current price occupies
Monitoring RSI for overbought/oversold conditions
Generating signals when RSI extremes coincide with key Fibonacci levels
Scoring confluence strength on a 0-5 scale
When price reaches the Golden Zone (50%-61.8%) while RSI shows oversold conditions in an uptrend, the probability of a bounce increases significantly.
Fibonacci Levels Explained
The indicator draws nine Fibonacci levels based on the most recent swing:
0% (Swing Low/High): The starting point of the move
23.6%: Shallow retracement - often seen in strong trends
38.2%: First significant support/resistance level
50%: Psychological midpoint of the move
61.8% (Golden Ratio): The most important Fibonacci level
78.6%: Deep retracement - last defense before trend failure
100% (Swing High/Low): The end point of the move
127.2% (TP1): First extension target for take profit
161.8% (TP2): Second extension target for take profit
The Golden Zone
The area between 50% and 61.8% is highlighted as the "Golden Zone" because:
It represents the optimal retracement depth for trend continuation
Institutional traders often place orders in this zone
It offers favorable risk-to-reward ratios
Price frequently bounces from this area in healthy trends
When price enters the Golden Zone, the indicator highlights it with a semi-transparent box and optional background coloring.
Pivot Detection System
The indicator uses a configurable pivot detection algorithm:
pivotDetect(float src, int len, bool isHigh) =>
int halfLen = len / 2
float centerVal = nz(src , src)
bool isPivot = true
for i = 0 to len - 1
if isHigh
if nz(src , src) > centerVal
isPivot := false
break
else
if nz(src , src) < centerVal
isPivot := false
break
isPivot ? centerVal : float(na)
This identifies swing highs and lows by checking if a bar's high/low is the highest/lowest within the specified depth on both sides.
Visual Components
1. Fibonacci Lines
Horizontal lines at each Fibonacci level:
Solid lines for major levels (0%, 50%, 61.8%, 100%)
Dashed lines for secondary levels (23.6%, 38.2%, 78.6%)
Dotted lines for extension levels (127.2%, 161.8%)
Color-coded for easy identification
Configurable line width
2. Fibonacci Labels
Price labels at each level showing:
Fibonacci percentage
Actual price at that level
Golden Zone label highlighted
TP1 and TP2 labels for targets
3. Golden Zone Box
A semi-transparent box highlighting the 50%-61.8% zone:
Gold colored border and fill
Extends from swing start to current bar (or beyond if extended)
Provides clear visual of the optimal entry zone
4. ZigZag Lines
Connecting lines between detected pivots:
Cyan for moves from low to high
Orange for moves from high to low
Helps visualize market structure
Configurable line width
5. Pivot Markers
Small labels at detected swing points:
"HH" (Higher High) at swing highs
"LL" (Lower Low) at swing lows
Helps track market structure
6. Entry Signals
BUY and SELL labels when confluence conditions are met:
BUY: RSI oversold + price in entry zone + uptrend + positive momentum
SELL: RSI overbought + price in entry zone + downtrend + negative momentum
Labels include "RSI+FIB" to indicate confluence
Confluence Scoring System
The indicator calculates a confluence score from 0 to 5:
+2 points: Price is in the Golden Zone (50%-61.8%)
+1 point: Price is in the entry zone (38.2%-61.8%)
+1 point: RSI is oversold in uptrend OR overbought in downtrend
+1 point: RSI divergence detected (bullish or bearish)
+1 point: Strong RSI momentum (change > 2 points)
Confluence ratings:
STRONG (4-5): Multiple factors align - high probability setup
MODERATE (2-3): Some factors align - proceed with caution
WEAK (0-1): Few factors align - wait for better setup
Dashboard Panel
The 10-row dashboard provides comprehensive analysis:
RSI Value: Current RSI reading (large text)
RSI State: OVERBOUGHT, OVERSOLD, BULLISH, BEARISH, or NEUTRAL
Fib Trend: UPTREND or DOWNTREND based on last pivot sequence
Price Zone: Current Fibonacci zone (e.g., "GOLDEN ZONE", "38.2% - 50%")
Price: Current close price (large text)
Confluence: Score rating with numeric value (e.g., "STRONG (4/5)")
Nearest Fib: Closest key Fibonacci level with price
TP1 (127.2%): First take profit target price
TP2 (161.8%): Second take profit target price
Input Parameters
Pivot Detection:
Pivot Depth: Bars to look back for swing detection (default: 10)
Min Deviation %: Minimum price move to confirm pivot (default: 1.0)
RSI Settings:
RSI Length: Period for RSI calculation (default: 14)
Source: Price source (default: close)
Overbought: Upper threshold (default: 70)
Oversold: Lower threshold (default: 30)
Fibonacci Display:
Show Fib Lines: Toggle Fibonacci lines (default: enabled)
Show Fib Labels: Toggle price labels (default: enabled)
Show Golden Zone Box: Toggle zone highlight (default: enabled)
Line Width: Thickness of Fibonacci lines (default: 2)
Extend Fib Lines: Extend lines into future (default: enabled)
ZigZag:
Show ZigZag: Toggle connecting lines (default: enabled)
ZigZag Width: Line thickness (default: 2)
Signals:
Show Entry Signals: Toggle BUY/SELL labels (default: enabled)
Show TP Levels: Toggle take profit in dashboard (default: enabled)
Show RSI-Fib Confluence: Toggle confluence analysis (default: enabled)
Dashboard:
Show Dashboard: Toggle information panel (default: enabled)
Position: Choose corner placement
Colors:
Bullish: Color for bullish elements (default: cyan)
Bearish: Color for bearish elements (default: orange)
Neutral: Color for neutral elements (default: gray)
Golden Zone: Color for Golden Zone highlight (default: gold)
How to Use RSI Fibonacci Flow
Identifying Entry Zones:
Wait for price to retrace to the 38.2%-61.8% zone
Check if RSI is approaching oversold (for longs) or overbought (for shorts)
Look for STRONG confluence rating in the dashboard
Enter when BUY or SELL signal appears
Setting Take Profit Targets:
TP1 at 127.2% extension for conservative target
TP2 at 161.8% extension for aggressive target
Consider scaling out at each level
Using the Price Zone:
"BELOW 23.6%" - Price hasn't retraced much; wait for deeper pullback
"23.6% - 38.2%" - Shallow retracement; strong trend continuation possible
"38.2% - 50%" - Good entry zone for trend trades
"GOLDEN ZONE" - Optimal entry zone; highest probability
"61.8% - 78.6%" - Deep retracement; trend may be weakening
"78.6% - 100%" - Very deep; trend reversal possible
"ABOVE/BELOW 100%" - Trend has likely reversed
Confluence Trading Strategy:
Only take trades with confluence score of 3 or higher
STRONG confluence (4-5) warrants larger position size
MODERATE confluence (2-3) warrants smaller position size
WEAK confluence (0-1) - wait for better setup
Alert Conditions
Ten alert conditions are available:
RSI-Fib BUY Signal: Strong bullish confluence detected
RSI-Fib SELL Signal: Strong bearish confluence detected
Price in Golden Zone: Price enters 50%-61.8% zone
New Pivot High: Swing high detected
New Pivot Low: Swing low detected
RSI Overbought: RSI crosses above overbought threshold
RSI Oversold: RSI crosses below oversold threshold
Bullish Divergence: Potential bullish RSI divergence
Bearish Divergence: Potential bearish RSI divergence
Strong Confluence: Confluence score reaches 4 or higher
Understanding Trend Direction
The indicator determines trend based on pivot sequence:
UPTREND: Last pivot was a low after a high (expecting move up)
DOWNTREND: Last pivot was a high after a low (expecting move down)
Fibonacci levels are drawn accordingly:
In uptrend: 0% at swing low, 100% at swing high
In downtrend: 0% at swing high, 100% at swing low
Bar Coloring
When confluence features are enabled:
Cyan bars on strong bullish signals
Orange bars on strong bearish signals
Gold-tinted bars when price is in Golden Zone
Best Practices
Use on 1H timeframe or higher for more reliable pivots
Adjust Pivot Depth based on timeframe (higher for longer timeframes)
Wait for price to enter Golden Zone before considering entries
Confirm RSI is in favorable territory before trading
Use extension levels (127.2%, 161.8%) for realistic profit targets
Combine with support/resistance and candlestick patterns
Higher confluence scores indicate higher probability setups
Limitations
Pivot detection has inherent lag (must wait for confirmation)
Fibonacci levels are subjective - different swings produce different levels
Works best in trending markets with clear swings
RSI can remain overbought/oversold in strong trends
Not all Golden Zone entries will be successful
The source code is open and available for review and modification.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Fibonacci levels are not guaranteed support/resistance - they are probability zones based on historical price behavior. Always conduct your own analysis and use proper risk management.
- Made with passion by officialjackofalltrades :D
Indicator
Red Bull Wings [JOAT]RED BULL WINGS - Bullish-Only Institutional Overlay
Introduction and Purpose
RED BULL WINGS is an open-source overlay indicator that combines five distinct bullish detection methods into a single composite scoring system. The core problem this indicator solves is that individual bullish signals (patterns, volume, zones, trendlines) often disagree or fire in isolation. A bullish engulfing pattern means little if volume is weak and price is far from support. Traders need confluence across multiple dimensions to identify high-probability setups.
This indicator addresses that by scoring each bullish component separately, then combining them into a weighted WINGS score (0-100) that reflects overall bullish conviction. When multiple components align, the score rises; when they disagree, the score stays low.
Why These Five Modules Work Together
Each module measures a different aspect of bullish market structure:
1. Module A - Bullish Candlestick Engine - Detects classic reversal patterns (engulfing, marubozu, hammer, 3-bar cluster). These patterns identify WHERE buyers are stepping in.
2. Module B - PVSRA Volume Climax - Measures spread x volume to detect institutional participation. This tells you WHETHER smart money is involved.
3. Module C - Demand Zone Detection - Identifies and tracks order block zones where buyers previously overwhelmed sellers. This shows you WHERE institutional support exists.
4. Module D - Trendline Channel - Builds dynamic support/resistance from pivot points. This reveals the STRUCTURE of the current trend.
5. Module E - Ichimoku Assist - Optional filter using Tenkan/Kijun cross, cloud position, and Chikou confirmation. This provides TREND PERMISSION context.
The combination works because:
Patterns alone can fail without volume confirmation
Volume alone means nothing without price structure context
Zones alone are static without pattern/volume triggers
Trendlines alone miss the micro-level entry timing
When 3+ modules agree, the probability of a valid bullish setup increases significantly
How the Calculations Work
Module A - Pattern Detection:
Bullish Engulfing - Current bullish bar completely engulfs prior bearish bar:
bool engulfingCond = isBullish() and
isBearish() and
open <= close and
close >= open and
bodySize() > bodySize()
Marubozu - Strong body with minimal wicks (body >= 1.8x average, wick ratio < 20%):
float wickRatio = candleRange() > 0 ? (upperWick() + lowerWick()) / candleRange() : 0
bool marubozuCond = isBullish() and
bodySize() >= bodySizeAvg * i_maruMult and
wickRatio < i_wickRatioMax
Hammer - Long lower wick (>= 2.5x body), close in upper third, volume confirmation:
bool hammerWick = lowerWick() >= i_hammerWickMult * bodySize()
bool hammerClose = close >= low + (candleRange() * 0.66)
bool hammerVol = volume >= i_pvsraRisingMult * volAvg
3-Bar Cluster - Three consecutive bullish closes with increasing prices and volume spike:
bool threeBarBullish = isBullish() and isBullish() and isBullish()
bool increasingCloses = close > close and close > close
bool volSpike3Bar = volume >= i_pvsraRisingMult * volAvg or
volume >= i_pvsraRisingMult * volAvg
Module B - PVSRA Volume Analysis:
Uses spread x volume to detect climax conditions:
float spreadVol = candleRange() * volume
float maxSpreadVol = ta.highest(spreadVol, ADJ_PVSRA_LOOKBACK)
bool volClimax = volume >= i_pvsraClimaxMult * volAvg or spreadVol >= maxSpreadVol
bool volRising = volume >= i_pvsraRisingMult * volAvg and volume < i_pvsraClimaxMult * volAvg
Volume only scores when the candle is bullish, preventing false signals on bearish volume spikes.
Module C - Demand Zone Detection:
Identifies zones using a two-candle structure:
// Small bearish candle A followed by larger bullish candle B
bool candleA_bearish = isBearish()
bool candleB_bullish = isBullish()
bool newZoneCond = candleA_bearish and candleB_bullish and
candleB_size >= i_zoneSizeMult * candleA_size
Zones are drawn as rectangles and tracked for retests. Score increases when price is near or inside an active zone, with bonus points for rejection candles.
Module D - Trendline Channel:
Builds dynamic channel from confirmed pivot points:
float ph = ta.pivothigh(high, i_pivotLeft, i_pivotRight)
float pl = ta.pivotlow(low, i_pivotLeft, i_pivotRight)
Pivots are stored and connected to form upper/lower channel lines. The indicator detects breakouts when price closes beyond the channel with volume confirmation.
Module E - Ichimoku Assist:
Standard Ichimoku calculations with bullish scoring:
float tenkan = (ta.highest(high, i_tenkanLen) + ta.lowest(low, i_tenkanLen)) / 2
float kijun = (ta.highest(high, i_kijunLen) + ta.lowest(low, i_kijunLen)) / 2
bool tkCross = ta.crossover(tenkan, kijun)
bool priceAboveCloud = close > cloudTop
bool chikouAbovePrice = chikou > close
Module F - WINGS Composite Score:
All module scores are combined using adjustable weights:
float WINGS_score = 100 * (nW_pattern * S_pattern +
nW_volume * S_vol +
nW_zone * S_zone +
nW_trend * S_trend +
nW_ichi * S_ichi)
Default weights: Pattern 30%, Volume 25%, Zone 20%, Trend 15%, Ichimoku 10%.
Signal Thresholds
WATCH (30-49) - Interesting bullish context forming, not yet actionable
MOMENTUM (50-74) - Strong bullish conditions, multiple modules agreeing
LIFT-OFF (75+) - High-confidence bullish confluence across most modules
WINGS Badge (Dashboard)
The right-side panel displays:
WINGS Score - Current composite score (0-100)
Pattern - Active pattern name and strength, or neutral placeholder
Volume - Normal / Rising / CLIMAX status
Zone - ACTIVE if price is near a demand zone
Trend - Channel position or BREAK status
Ichimoku - OFF / Weak / Bullish / STRONG
Status - Overall signal level (Neutral / WATCH / MOMENTUM / LIFT-OFF)
Input Parameters
Module Toggles:
Enable Bullish Patterns (true) - Toggle pattern detection
Enable PVSRA Volume (true) - Toggle volume analysis
Enable Order Blocks (true) - Toggle demand zone detection
Enable Trendlines (true) - Toggle pivot channel
Enable Ichimoku Assist (false) - Toggle Ichimoku filter (off by default for performance)
Enable Visual Effects (false) - Toggle labels, trails, and visual elements
LIVE MODE (false) - Enable intrabar signals (WARNING: signals may repaint)
Pattern Engine:
Pattern Lookback (5) - Bars for body size averaging
Marubozu Body Multiplier (1.8) - Minimum body size vs average
Hammer Wick Multiplier (2.5) - Minimum lower wick vs body
Max Wick Ratio (0.2) - Maximum wick percentage for marubozu
Volume / PVSRA:
PVSRA Lookback (10) - Period for volume averaging
Climax Multiplier (2.0) - Volume threshold for climax detection
Rising Volume Multiplier (1.5) - Volume threshold for rising detection
Order Blocks:
Zone Size Multiplier (2.0) - Minimum bullish candle size vs bearish
Zone Extend Bars (200) - How far zones project forward
Max Zones (12) - Maximum active zones displayed
Remove Zone on Close Below (true) - Delete broken zones
Trendlines:
Pivot Left/Right Bars (3/3) - Pivot detection sensitivity
Min Slope % (0.25) - Minimum trendline angle
Max Trendlines (5) - Maximum pivot points stored
Trendline Projection Bars (60) - Forward projection distance
Ichimoku:
Tenkan Length (9) - Conversion line period
Kijun Length (26) - Base line period
Senkou B Length (52) - Leading span B period
Displacement (26) - Cloud displacement
WINGS Score:
Weight: Pattern (0.30) - Pattern contribution to score
Weight: Volume (0.25) - Volume contribution to score
Weight: Zone (0.20) - Zone contribution to score
Weight: Trend (0.15) - Trendline contribution to score
Weight: Ichimoku (0.10) - Ichimoku contribution to score
Lift-Off Threshold (75) - Score required for LIFT-OFF signal
Momentum Watch Threshold (50) - Score required for MOMENTUM signal
Visuals:
Signal Cooldown (8) - Minimum bars between labels
Show WINGS Score Badge (true) - Toggle dashboard
Show Wing Combos (true) - Show DOUBLE/MEGA WINGS streaks
Red Background Wash (true) - Tint chart background
Show Lift-Off Trails (false) - Toggle golden trail visuals
How to Use This Indicator
For Bullish Entry Identification:
1. Monitor the WINGS badge for score changes
2. Wait for MOMENTUM (50+) or LIFT-OFF (75+) signals
3. Check which modules are contributing (Pattern + Volume + Zone = stronger)
4. Use demand zones and trendlines as structural reference for entries
For Confluence Confirmation:
1. Use alongside your existing analysis
2. LIFT-OFF signals indicate multiple bullish factors aligning
3. Low scores (< 30) suggest weak bullish context even if one factor looks good
For Zone-Based Trading:
1. Watch for price approaching active demand zones
2. Look for pattern + volume confirmation at zone retests
3. Zone score increases with successful retests
For Trendline Analysis:
1. Monitor the pivot-based channel for trend structure
2. Breakouts with volume confirmation trigger TREND BREAK alerts
3. Price inside channel with bullish patterns = trend continuation setup
1M and lower timeframes:
Alerts Available
LIFT-OFF - High-confidence bullish confluence
MOMENTUM - Strong bullish conditions
Zone Retest - Bullish rejection from demand zone
Trendline Break - Breakout with volume confirmation
Individual patterns (Engulfing, Marubozu, Hammer, 3-Bar Cluster)
Volume Climax - Institutional volume spike
DOUBLE WINGS / MEGA WINGS - Consecutive lift-off signals
Repainting Behavior
By default, the indicator uses confirmed bars only (barstate.isconfirmed), meaning signals appear after the bar closes and do not repaint. However:
LIVE MODE - When enabled, signals can appear intrabar but may disappear if conditions change before bar close. A warning label displays when LIVE MODE is active.
Trendlines - Pivot detection requires lookback bars, so the most recent trendline segments may adjust as new pivots confirm. This is inherent to pivot-based analysis.
Demand Zones - Zones are created on confirmed bars and do not repaint, but they can be removed if price closes below the zone bottom (configurable).
Live Mode with 'Enable Visual Effect' turned off in settings:
Limitations
This is a bullish-only indicator. It does not detect bearish setups or provide short signals.
The WINGS score is a confluence measure, not a prediction. High scores indicate favorable conditions, not guaranteed outcomes.
Pattern detection uses simplified logic. Not all candlestick nuances are captured.
Volume analysis requires reliable volume data. Results may vary on instruments with inconsistent volume reporting.
Ichimoku calculations add processing overhead. Disable if not needed.
Demand zones are based on a specific two-candle structure. Other valid zones may not be detected.
Trendlines use linear regression between pivots. Curved or complex channels are not supported.
Timeframe Recommendations
15m-1H: More frequent signals, useful for intraday analysis. Higher noise.
4H-Daily: Best balance of signal quality and frequency for swing trading.
Weekly: Fewer but more significant signals for position trading.
Adjust lookback periods and thresholds based on your timeframe. Shorter timeframes may benefit from shorter lookbacks.
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 module works.
This indicator does not constitute financial advice. The WINGS score and signals do not guarantee profitable trades. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses. Test thoroughly on your preferred instruments and timeframes before using in live trading.
- Made with passion by officialjackofalltrades
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