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Indicator
Indicator
Prism Band Dynamics [JOAT]Prism Band Dynamics - Bollinger-Style Bands with Force Detection
Introduction and Purpose
Prism Band Dynamics is an open-source overlay indicator that creates dynamic Bollinger-style bands with an innovative "force detection" system. The core problem this indicator solves is that standard Bollinger Bands show volatility but don't indicate directional momentum. When all three band components (upper, lower, basis) move in the same direction, it indicates strong directional force that standard bands don't highlight.
This indicator addresses that by detecting when all band components align directionally, providing a clear signal of market force.
Why Force Detection Matters
Standard Bollinger Bands expand and contract based on volatility, but they don't tell you about directional momentum. Force detection adds this dimension:
1. Bullish Force - Upper band, lower band, AND basis all moving up together. This indicates strong upward momentum where even the lower support level is rising.
2. Bearish Force - Upper band, lower band, AND basis all moving down together. This indicates strong downward momentum where even the upper resistance level is falling.
3. Neutral - Mixed movement indicates consolidation or uncertainty.
How Force Detection Works
bool upperUp = upper > upper
bool lowerUp = lower > lower
bool basisUp = basis > basis
int forceFull = if upperUp and lowerUp and basisUp
1 // Bullish force
else if upperDn and lowerDn and basisDn
-1 // Bearish force
else
0 // Neutral
Additional Features
Squeeze Detection - Identifies when band width contracts below threshold, often preceding large moves
Gradient Fills - Color intensity reflects force strength
Direction Change Arrows - Visual markers when force direction shifts
Dashboard Information
Force - Current force status (BULLISH/BEARISH/NEUTRAL)
Position - Price location within bands (Upper/Mid/Lower Zone)
Band Width - Current width percentage with expansion/contraction label
Volatility - Squeeze status (SQUEEZE/NORMAL)
Force Count - Bars since last force change
How to Use This Indicator
For Trend Following:
1. Enter long when force turns BULLISH
2. Enter short when force turns BEARISH
3. Exit or reduce when force turns NEUTRAL
For Squeeze Breakouts:
1. Watch for SQUEEZE status in dashboard
2. Prepare for breakout in either direction
3. Enter when force confirms direction after squeeze
For Mean Reversion:
1. Only trade mean-reversion when force is NEUTRAL
2. Avoid fading moves when force is active
3. Use band touches as entry points during neutral force
Input Parameters
Length (20) - Period for basis and standard deviation
Multiplier (2.0) - Standard deviation multiplier for bands
MA Type (SMA) - Basis calculation method
Squeeze Threshold (0.5) - Band width percentage for squeeze detection
Timeframe Recommendations
4H-Daily: Cleanest force signals
1H: Good balance of signals and reliability
15m: More signals but more noise
Limitations
Force detection can lag during rapid reversals
Squeeze breakouts can fail (false breakouts)
Works best in markets with clear trending/ranging phases
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Force detection does not guarantee trend continuation. Always use proper risk management.
- Made with passion by officialjackofalltrades
Indicator
Indicator
Indicator
Eclipse Multi-Oscillator [JOAT]Eclipse Multi-Oscillator - Unified Momentum Confluence System
Introduction and Purpose
Eclipse Multi-Oscillator is an open-source indicator that combines four classic oscillators (RSI, Stochastic, CCI, and Williams %R) into a single unified view with confluence detection. The core problem this indicator solves is oscillator disagreement: traders often see RSI oversold while Stochastic is neutral, or CCI overbought while Williams %R is mid-range. This creates confusion about the true momentum state.
This indicator addresses that by displaying all four oscillators together and counting how many agree on overbought or oversold conditions, providing a clear confluence score that cuts through the noise.
Why These Four Oscillators Work Together
Each oscillator measures momentum differently, and their combination provides a more complete picture:
1. RSI (Relative Strength Index) - Measures the magnitude of recent price changes. Best at identifying momentum exhaustion.
2. Stochastic - Compares closing price to the high-low range. Best at identifying where price is within its recent range.
3. CCI (Commodity Channel Index) - Measures price deviation from statistical mean. Best at identifying unusual price movements.
4. Williams %R - Similar to Stochastic but inverted. Provides confirmation of Stochastic readings.
When 3 or more of these oscillators agree on overbought or oversold, the signal is significantly more reliable than any single oscillator alone.
How Confluence Scoring Works
The indicator counts how many oscillators are in extreme territory:
int obCount = 0
if rsi > rsiOB
obCount += 1
if stochK > stochOB
obCount += 1
if cci > cciOB
obCount += 1
if willRScaled > stochOB
obCount += 1
bool strongOverbought = obCount >= 3
bool strongOversold = osCount >= 3
The confluence score ranges from -4 (all oversold) to +4 (all overbought), with 0 being neutral.
Signal Types
Strong Oversold - 3+ oscillators below oversold threshold (potential bounce)
Strong Overbought - 3+ oscillators above overbought threshold (potential pullback)
OB/OS Exit - RSI leaving extreme zone with Stochastic confirmation (potential reversal)
Divergence - Price makes new high/low while RSI does not (potential reversal warning)
Dashboard Information
RSI/Stoch K/CCI/Will %R - Current values with zone status (OB/OS/MID)
Confluence - Overall bias (STRONG OS, STRONG OB, Lean Bull/Bear, Neutral)
OB Count - How many oscillators are overbought (0-4)
OS Count - How many oscillators are oversold (0-4)
How to Use This Indicator
For Reversal Trading:
1. Wait for Strong Oversold (3+ oscillators agree)
2. Look for bullish candlestick pattern or support level
3. Enter long with stop below recent low
4. Take profit when confluence returns to neutral or overbought
For Trend Confirmation:
1. Check confluence direction matches your trade bias
2. Avoid longs when confluence is strongly overbought
3. Avoid shorts when confluence is strongly oversold
For Divergence Trading:
1. Watch for "D" labels indicating RSI divergence
2. Bullish divergence at support = potential long
3. Bearish divergence at resistance = potential short
Input Parameters
RSI Length (14) - Period for RSI calculation
Stochastic K/D Length (14/3) - Periods for Stochastic
CCI Length (20) - Period for CCI
Williams %R Length (14) - Period for Williams %R
OB/OS Thresholds - Customizable levels for each oscillator
Timeframe Recommendations
15m-1H: Good for intraday momentum analysis
4H-Daily: Best for swing trading confluence
Very short timeframes may produce noisy signals
Limitations
All oscillators can remain in extreme territory during strong trends
Confluence does not predict direction, only identifies extremes
Divergence detection is simplified and may miss some patterns
Works best in ranging or moderately trending markets
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied.
This indicator does not constitute financial advice. Oscillator confluence does not guarantee reversals. Past performance does not guarantee future results. Always use proper risk management.
- Made with passion by officialjackofalltrades
Indicator
Aurora Volatility Bands [JOAT]Aurora Volatility Bands - Dynamic ATR-Based Envelope System
Introduction and Purpose
Aurora Volatility Bands is an open-source overlay indicator that creates multi-layered volatility envelopes around price using ATR (Average True Range) calculations. The core problem this indicator solves is that static bands (like fixed percentage envelopes) fail to adapt to changing market conditions. During high volatility, static bands are too tight; during low volatility, they're too wide.
This indicator addresses that by using ATR-based dynamic bands that automatically expand during volatile periods and contract during quiet periods, providing contextually appropriate support/resistance levels at all times.
Why These Components Work Together
The indicator combines three analytical approaches:
1. Triple-Layer Band System - Inner (1x ATR), Outer (2x ATR), and Extreme (3x ATR) bands provide graduated levels of significance
2. Volatility State Detection - Compares current ATR to historical average to classify market regime
3. Multiple MA Types - Allows customization of the center line calculation method
These components complement each other:
The triple-layer system gives traders multiple reference points - inner bands for normal moves, outer for significant moves, extreme for rare events
Volatility state detection tells you WHEN bands are expanding or contracting, helping anticipate breakouts or mean-reversion
MA type selection lets you match the indicator to your trading style (faster EMA vs smoother SMA)
How the Calculation Works
The bands are calculated using ATR multiplied by configurable factors:
float atr = ta.atr(atrPeriod)
float innerUpper = centerMA + (atr * innerMult)
float outerUpper = centerMA + (atr * outerMult)
float extremeUpper = centerMA + (atr * extremeMult)
Volatility state is determined by comparing current ATR percentage to its historical average:
float atrPercent = (atr / close) * 100
float avgAtrPercent = ta.sma(atrPercent, volatilityLookback)
float volatilityRatio = atrPercent / avgAtrPercent
bool isExpanding = volatilityRatio > 1.2 // 20%+ above average
bool isContracting = volatilityRatio < 0.8 // 20%+ below average
Signal Types
Band Touch - Price reaches inner, outer, or extreme bands
Mean Reversion - Price returns to center after touching outer/extreme bands
Breakout - Sustained move beyond outer bands during volatility expansion
Dashboard Information
Volatility - Current state (EXPANDING/CONTRACTING/NORMAL)
Vol Ratio - Current volatility vs average (e.g., 1.5x = 50% above average)
ATR - Current ATR value
ATR % - ATR as percentage of price
Zone - Current price position (EXTREME HIGH/UPPER ZONE/CENTER ZONE/etc.)
Position - Price position as percentage within band structure
Width - Total band width as percentage of price
Using SMA in settings:
How to Use This Indicator
For Mean-Reversion Trading:
1. Wait for price to touch outer or extreme bands
2. Check that volatility state is NORMAL or CONTRACTING (not expanding)
3. Look for reversal candlestick patterns at the band
4. Enter toward center MA with stop beyond the band
For Breakout Trading:
1. Wait for volatility state to show EXPANDING
2. Look for price closing beyond outer bands
3. Enter in direction of breakout
4. Use the band as trailing stop reference
For Volatility Analysis:
1. Monitor volatility ratio for regime changes
2. CONTRACTING often precedes large moves (squeeze)
3. EXPANDING confirms trend strength
Using VWMA and Mean Reversion Signal/MR:
Input Parameters
ATR Period (14) - Period for ATR calculation
Inner/Outer/Extreme Multipliers (1.0/2.0/3.0) - Band distance from center
MA Type (EMA) - Center line calculation method
MA Period (20) - Period for center line
Volatility Comparison Period (20) - Lookback for volatility state
Timeframe Recommendations
15m-1H: Good for intraday mean-reversion
4H-Daily: Best for swing trading and breakout identification
Weekly: Useful for position trading and major level identification
Limitations
ATR-based bands lag during sudden volatility spikes
Mean-reversion signals can fail in strong trends
Breakout signals may whipsaw in ranging markets
Works best on liquid instruments with consistent volatility patterns
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works.
This indicator does not constitute financial advice. Band touches do not guarantee reversals. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses.
- Made with passion by officialjackofalltrades Indicator
Quantum Reversal Detector [JOAT]
Quantum Reversal Detector - Multi-Factor Reversal Probability Analysis
Introduction and Purpose
Quantum Reversal Detector is an open-source overlay indicator that combines multiple reversal detection methods into a unified probability-based framework. The core problem this indicator addresses is the unreliability of single-factor reversal signals. A price touching support means nothing without momentum confirmation; an RSI oversold reading means nothing without price structure context.
This indicator solves that by requiring multiple independent factors to align before generating reversal signals, then expressing the result as a probability score rather than a binary signal.
Why These Components Work Together
The indicator combines five analytical approaches, each addressing a different aspect of reversal detection:
1. RSI Extremes - Identifies momentum exhaustion (overbought/oversold)
2. MACD Crossovers - Confirms momentum direction change
3. Support/Resistance Proximity - Ensures price is at a significant level
4. Multi-Depth Momentum - Analyzes momentum across multiple timeframes
5. Statistical Probability - Quantifies reversal likelihood using Bayesian updating
These components are not randomly combined. Each filter catches reversals that others miss:
RSI catches momentum exhaustion but misses structural reversals
MACD catches momentum shifts but lags price action
S/R proximity catches structural levels but ignores momentum
Multi-depth momentum catches divergences across timeframes
Probability scoring combines all factors into actionable confidence levels
How the Detection System Works
Step 1: Pattern Detection
The indicator first identifies potential reversal conditions:
// Check if price is at support/resistance
float lowestLow = ta.lowest(low, period)
float highestHigh = ta.highest(high, period)
bool atSupport = low <= lowestLow * 1.002
bool atResistance = high >= highestHigh * 0.998
// Check RSI conditions
float rsi = ta.rsi(close, 14)
bool oversold = rsi < 30
bool overbought = rsi > 70
// Check MACD crossover
float macd = ta.ema(close, 12) - ta.ema(close, 26)
float signal = ta.ema(macd, 9)
bool macdBullish = ta.crossover(macd, signal)
bool macdBearish = ta.crossunder(macd, signal)
// Combine for reversal detection
if atSupport and oversold and macdBullish
bullishReversal := true
Step 2: Multi-Depth Momentum Analysis
The indicator calculates momentum across multiple periods to detect divergences:
calculateQuantumMomentum(series float price, simple int period, simple int depth) =>
float totalMomentum = 0.0
for i = 0 to depth - 1
int currentPeriod = period * (i + 1)
float momentum = ta.roc(price, currentPeriod)
totalMomentum += momentum
totalMomentum / depth
This creates a composite momentum reading that smooths out noise while preserving genuine momentum shifts.
Step 3: Bayesian Probability Calculation
The indicator uses Bayesian updating to calculate reversal probability:
bayesianProbability(series float priorProb, series float likelihood, series float evidence) =>
float posterior = evidence > 0 ? (likelihood * priorProb) / evidence : priorProb
math.min(math.max(posterior, 0.0), 1.0)
The prior probability starts at 50% and updates based on:
RSI extreme readings increase likelihood
MACD crossovers increase likelihood
S/R proximity increases likelihood
Momentum divergence increases likelihood
Step 4: Confidence Intervals
Using Monte Carlo simulation concepts, the indicator estimates price distribution:
monteCarloSimulation(series float price, series float volatility, simple int iterations) =>
float sumPrice = 0.0
float sumSqDiff = 0.0
for i = 0 to iterations - 1
float randomFactor = (i % 10 - 5) / 10.0
float simulatedPrice = price + volatility * randomFactor
sumPrice += simulatedPrice
float avgPrice = sumPrice / iterations
// Calculate standard deviation for confidence intervals
This provides 95% and 99% confidence bands around the current price.
Signal Classification
Signals are classified by confirmation level:
Confirmed Reversal : Pattern detected for N consecutive bars (default 3)
High Probability : Confirmed + Bayesian probability > 70%
Ultra High Probability : High probability + PDF above average
Dashboard Information
The dashboard displays:
Bayesian Probability - Updated reversal probability (0-100%)
Quantum Momentum - Multi-depth momentum average
RSI - Current RSI value with overbought/oversold status
Volatility - Current ATR as percentage of price
Reversal Signal - BULLISH, BEARISH, or NONE
Divergence - Momentum divergence detection
MACD - Current MACD histogram value
S/R Zone - AT SUPPORT, AT RESISTANCE, or NEUTRAL
95% Confidence - Price range with 95% probability
Bull/Bear Targets - ATR-based reversal targets
Visual Elements
Quantum Bands - ATR-based upper and lower channels
Probability Field - Circle layers showing probability distribution
Confidence Bands - 95% and 99% confidence interval circles
Reversal Labels - REV markers at confirmed reversals
High Probability Markers - Star diamonds at high probability setups
Reversal Zones - Boxes around confirmed reversal areas
Divergence Markers - Triangles at momentum divergences
How to Use This Indicator
For Reversal Trading:
1. Wait for Bayesian Probability to exceed 70%
2. Confirm price is at S/R zone (dashboard shows AT SUPPORT or AT RESISTANCE)
3. Check that RSI is in extreme territory (oversold for longs, overbought for shorts)
4. Enter when REV label appears with high probability marker
For Risk Management:
1. Use the 95% confidence band as a stop-loss reference
2. Use Bull/Bear Targets for take-profit levels
3. Higher probability readings warrant larger position sizes
For Filtering False Signals:
1. Increase Confirmation Bars to require more consecutive signals
2. Only trade when probability exceeds 70%
3. Require divergence confirmation for highest conviction
Input Parameters
Reversal Period (21) - Lookback for S/R and momentum calculations
Quantum Depth (5) - Number of momentum layers for multi-depth analysis
Confirmation Bars (3) - Consecutive bars required for confirmation
Detection Sensitivity (1.2) - Band width and target multiplier
Bayesian Probability (true) - Enable probability calculation
Monte Carlo Simulation (true) - Enable confidence interval calculation
Normal Distribution (true) - Enable PDF calculation
Confidence Intervals (true) - Enable confidence bands
Timeframe Recommendations
1H-4H: Best for swing trading reversals
Daily: Fewer but more significant reversal signals
15m-30m: More signals, requires higher probability threshold
Limitations
Statistical concepts are simplified implementations for Pine Script
Monte Carlo uses deterministic pseudo-random factors, not true randomness
Bayesian probability uses simplified prior/likelihood model
Reversal detection does not guarantee actual reversals will occur
Confirmation bars add lag to signal generation
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works.
This indicator does not constitute financial advice. Reversal detection is probabilistic, not predictive. The probability scores represent statistical likelihood based on historical patterns, not guaranteed outcomes. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses.
- Made with passion by officialjackofalltrades
Indicator
Photon Price Action Scanner [JOAT]Photon Price Action Scanner - Multi-Pattern Recognition with Adaptive Filtering
Introduction and Purpose
Photon Price Action Scanner is an open-source overlay indicator that automates the detection of 15+ candlestick patterns while filtering them through multiple confirmation layers. The core problem this indicator solves is pattern noise: raw candlestick pattern detection produces too many signals, most of which fail because they lack context. This indicator addresses that by combining pattern recognition with trend alignment, volume-weighted strength scoring, velocity confirmation, and an adaptive neural bias filter.
The combination of these components is not arbitrary. Each filter addresses a specific weakness in standalone pattern detection:
Trend alignment ensures patterns appear in favorable market structure
Volume-weighted strength filters out weak patterns with low conviction
Velocity confirmation identifies momentum behind the pattern
Neural bias filter adapts to recent price behavior to avoid counter-trend signals
What Makes This Indicator Original
While candlestick pattern scanners exist, this indicator's originality comes from:
1. Multi-Layer Filtering System - Patterns must pass through trend, strength, velocity, and neural bias filters before generating signals. This dramatically reduces false positives compared to simple pattern detection.
2. Adaptive Neural Bias Filter - A custom momentum-adjusted EMA that learns from recent price action using a configurable learning rate. This is not a standard moving average but an adaptive filter that accelerates during trends and smooths during consolidation.
3. Pattern Strength Scoring - Each pattern receives a strength score based on volume ratio and body size, allowing traders to focus on high-conviction setups rather than every pattern occurrence.
4. Smart Cooldown System - Prevents signal overlap by enforcing minimum bar spacing between pattern labels, keeping charts clean even when "Show All Patterns" is enabled.
How the Components Work Together
Step 1: Pattern Detection
The indicator scans for 15 candlestick patterns using precise mathematical definitions:
// Example: Bullish Engulfing requires the current bullish candle to completely
// engulf the previous bearish candle with a larger body
isBullishEngulfing() =>
bool pattern = close < open and close > open and
open <= close and close >= open and
close - open > open - close
pattern
// Example: Three White Soldiers requires three consecutive bullish candles
// with each opening within the previous body and closing higher
isThreeWhiteSoldiers() =>
bool pattern = close > open and close > open and close > open and
close < close and close < close and
open > open and open < close and
open > open and open < close
pattern
Step 2: Strength Calculation
Each detected pattern receives a strength score combining volume and body size:
float volRatio = avgVolume > 0 ? volume / avgVolume : 1.0
float bodySize = math.abs(close - open) / close
float baseStrength = (volRatio + bodySize * 100) / 2
This ensures patterns with above-average volume and large bodies score higher than weak patterns on low volume.
Step 3: Trend Alignment
Patterns are checked against the trend direction using an EMA:
float trendEMA = ta.ema(close, i_trendPeriod)
int trendDir = close > trendEMA ? 1 : close < trendEMA ? -1 : 0
Bullish patterns in uptrends and bearish patterns in downtrends receive priority.
Step 4: Neural Bias Filter
The adaptive filter uses a momentum-adjusted EMA that responds to price changes:
neuralEMA(series float src, simple int period, simple float lr) =>
var float neuralValue = na
var float momentum = 0.0
if na(neuralValue)
neuralValue := src
float error = src - neuralValue
float adjustment = error * lr
momentum := momentum * 0.9 + adjustment * 0.1
neuralValue := neuralValue + adjustment + momentum
neuralValue
The learning rate (lr) controls how quickly the filter adapts. Higher values make it more responsive; lower values make it smoother.
Step 5: Velocity Confirmation
Price velocity (rate of change) must exceed the average velocity for strong signals:
float velocity = ta.roc(close, i_trendPeriod)
float avgVelocity = ta.sma(velocity, i_trendPeriod)
bool velocityBull = velocity > avgVelocity * 1.5
Step 6: Signal Classification
Signals are classified based on how many filters they pass:
Strong Pattern : Pattern + strength threshold + trend alignment + neural bias + velocity
Ultra Pattern : Strong pattern + gap in same direction + velocity confirmation
Watch Pattern : Pattern detected but not all filters passed
Detected Patterns
Classic Reversal Patterns:
Bullish/Bearish Engulfing - Complete body engulfment with larger body
Hammer - Long lower wick (2x body), small upper wick, bullish context
Shooting Star - Long upper wick (2x body), small lower wick, bearish context
Morning Star - Three-bar bullish reversal with small middle body
Evening Star - Three-bar bearish reversal with small middle body
Piercing Line - Bullish candle closing above midpoint of previous bearish candle
Dark Cloud Cover - Bearish candle closing below midpoint of previous bullish candle
Bullish/Bearish Harami - Small body contained within previous larger body
Doji - Body less than 10% of total range (indecision)
Advanced Patterns (Optional):
Three White Soldiers - Three consecutive bullish candles with rising closes
Three Black Crows - Three consecutive bearish candles with falling closes
Tweezer Top - Equal highs with reversal candle structure
Tweezer Bottom - Equal lows with reversal candle structure
Island Reversal - Gap isolation creating reversal structure
Dashboard Information
The dashboard displays real-time analysis:
Pattern - Current detected pattern name or "SCANNING..."
Bull/Bear Strength - Volume-weighted strength scores
Trend - UPTREND, DOWNTREND, or SIDEWAYS based on EMA
RSI - 14-period RSI for momentum context
Momentum - 10-period momentum reading
Volatility - ATR as percentage of price
Neural Bias - BULLISH, BEARISH, or NEUTRAL from adaptive filter
Action - ULTRA BUY/SELL, BUY/SELL, WATCH BUY/SELL, or WAIT
Visual Elements
Pattern Labels - Abbreviated codes (BE=Engulfing, H=Hammer, MS=Morning Star, etc.)
Neural Bias Line - Adaptive trend line showing filter direction
Gap Boxes - Cyan boxes highlighting price gaps
Action Zones - Dashed boxes around strong pattern areas
Velocity Markers - Small circles when velocity confirms direction
Ultra Signals - Large labels for highest conviction setups
How to Use This Indicator
For Reversal Trading:
1. Wait for a pattern to appear at a key support/resistance level
2. Check that the Action shows "BUY" or "SELL" (not just "WATCH")
3. Confirm the Neural Bias aligns with your trade direction
4. Use the strength score to gauge conviction (higher is better)
For Trend Continuation:
1. Identify the trend using the Trend row in the dashboard
2. Look for patterns that align with the trend (bullish patterns in uptrends)
3. Ultra signals indicate the strongest continuation setups
For Filtering Noise:
1. Keep "Show All Patterns" disabled to see only filtered signals
2. Increase "Pattern Strength Filter" to see fewer, higher-quality patterns
3. Enable "Velocity Confirmation" to require momentum behind patterns
Input Parameters
Scan Sensitivity (1.0) - Overall detection sensitivity multiplier
Pattern Strength Filter (3) - Minimum strength score for strong signals
Trend Period (20) - EMA period for trend determination
Show All Patterns (false) - Display all patterns regardless of filters
Advanced Patterns (true) - Enable soldiers/crows/tweezer detection
Gap Analysis (true) - Enable gap detection and boxes
Velocity Confirmation (true) - Require velocity for strong signals
Neural Bias Filter (true) - Enable adaptive trend filter
Neural Period (50) - Lookback for neural bias calculation
Neural Learning Rate (0.12) - Adaptation speed (0.01-0.5)
Timeframe Recommendations
1H-4H: Best balance of signal frequency and reliability
Daily: Fewer but more significant patterns
15m-30m: More signals, requires tighter filtering (increase strength threshold)
Limitations
Pattern detection is mechanical and does not consider fundamental context
Neural bias filter may lag during rapid trend reversals
Gap detection requires clean price data without after-hours gaps
Strength scoring favors high-volume patterns, which may miss valid low-volume setups
- Made with passion by officialjackofalltrades
Indicator
Cosmic Volume Analyzer [JOAT]
Cosmic Volume Analyzer - Astrophysics Edition
Overview
Cosmic Volume Analyzer is an open-source oscillator indicator that applies astrophysics-inspired concepts to volume analysis. It classifies volume into buy/sell categories, calculates volume flow, detects accumulation/distribution phases, identifies climax volume events, and uses gravitational and stellar mass analogies to visualize volume dynamics.
What This Indicator Does
The indicator calculates and displays:
Volume Classification - Categorizes each bar as CLIMAX_BUY, CLIMAX_SELL, HIGH_BUY, HIGH_SELL, NORMAL_BUY, or NORMAL_SELL
Volume Flow - Percentage showing buy vs sell pressure over a lookback period
Buy/Sell Volume - Separated volume based on candle direction
Accumulation/Distribution - Phase detection using Money Flow Multiplier
Volume Oscillator - Fast vs slow volume EMA comparison
Gravitational Pull - Volume-weighted price attraction metric
Stellar Mass Index - Volume ratio combined with price momentum
Black Hole Detection - Identifies extremely low volume periods (liquidity voids)
Supernova Events - Detects extreme volume with extreme price movement
Orbital Cycles - Sine-wave based cyclical visualization
How It Works
Volume classification uses volume ratio and candle direction:
classifyVolume(series float vol, series float close, series float open) =>
float avgVol = ta.sma(vol, 20)
float volRatio = avgVol > 0 ? vol / avgVol : 1.0
if volRatio > 1.5
if close > open
classification := "CLIMAX_BUY"
else
classification := "CLIMAX_SELL"
else if volRatio > 1.2
// HIGH_BUY or HIGH_SELL
else
// NORMAL_BUY or NORMAL_SELL
Volume flow separates buy and sell volume over a period:
calculateVolumeFlow(series float vol, series float close, simple int period) =>
float currentBuyVol = close > open ? vol : 0.0
float currentSellVol = close < open ? vol : 0.0
// Accumulate in buffers
float flow = (buyVolume - sellVolume) / totalVol * 100
Accumulation/Distribution uses the Money Flow Multiplier:
float mfm = ((close - low) - (high - close)) / (high - low)
float mfv = mfm * vol
float adLine = ta.cum(mfv)
if adLine > adEMA and ta.rising(adLine, 3)
phase := "ACCUMULATION"
else if adLine < adEMA and ta.falling(adLine, 3)
phase := "DISTRIBUTION"
Gravitational pull uses volume-weighted price distance:
gravitationalPull(series float vol, series float price, simple int period) =>
float massCenter = ta.vwma(price, period)
float distance = math.abs(price - massCenter)
float mass = vol / ta.sma(vol, period)
float gravity = distance > 0 ? mass / (distance * distance) : 0.0
Signal Generation
Signals are generated based on volume conditions:
Buy Climax: Volume exceeds 2 standard deviations above average on bullish candle
Sell Climax: Volume exceeds 2 standard deviations above average on bearish candle
Strong Buy Flow: Volume flow exceeds positive threshold (default 45%)
Strong Sell Flow: Volume flow exceeds negative threshold (default -45%)
Supernova: Volume 3x average AND price change 3x average
Black Hole: Volume 2 standard deviations below average
Dashboard Panel (Top-Right)
Volume Class - Current volume classification
Volume Flow - Buy/sell flow percentage
Buy Volume - Accumulated buy volume
Sell Volume - Accumulated sell volume
A/D Phase - ACCUMULATION/DISTRIBUTION/NEUTRAL
Volume Strength - Normalized volume strength
Gravity Pull - Current gravitational metric
Stellar Mass - Current stellar mass index
Cosmic Field - Combined cosmic field strength
Black Hole - Detection status and void strength
Signal - Current actionable status
Visual Elements
Volume Ratio Columns - Colored bars showing normalized volume
Volume Flow Line - Main oscillator showing flow direction
Flow EMA - Smoothed flow for trend reference
Volume Oscillator - Area plot showing fast/slow comparison
Gravity Field - Area plot showing gravitational pull
Orbital Cycle - Circle plots showing cyclical pattern
Stellar Mass Line - Line showing mass index
Climax Markers - Fire emoji for buy climax, snowflake for sell climax
Supernova Markers - Diamond shapes for extreme events
Black Hole Markers - X-cross for liquidity voids
A/D Phase Background - Subtle background color based on phase
Input Parameters
Volume Period (default: 20) - Period for volume calculations
Distribution Levels (default: 5) - Granularity of distribution analysis
Flow Threshold (default: 1.5) - Multiplier for flow significance
Accumulation Period (default: 14) - Period for A/D calculation
Gravitational Analysis (default: true) - Enable gravity metrics
Black Hole Detection (default: true) - Enable void detection
Stellar Mass Calculation (default: true) - Enable mass index
Orbital Cycles (default: true) - Enable cyclical visualization
Supernova Detection (default: true) - Enable extreme event detection
Suggested Use Cases
Identify accumulation phases for potential long entries
Watch for distribution phases as potential exit signals
Use climax volume as potential exhaustion indicators
Monitor volume flow for directional bias
Avoid trading during black hole (low liquidity) periods
Watch for supernova events as potential trend acceleration
Timeframe Recommendations
Best on 15m to Daily charts. Volume analysis requires sufficient trading activity for meaningful readings.
Limitations
Volume data quality varies by exchange and instrument
Buy/sell separation is based on candle direction, not actual order flow
Astrophysics concepts are analogies, not literal physics
A/D phase detection may lag during rapid transitions
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management.
- Made with passion by officialjackofalltrades
Indicator
Fractal Market Geometry [JOAT]
Fractal Market Geometry
Overview
Fractal Market Geometry is an open-source overlay indicator that combines fractal analysis with harmonic pattern detection, Fibonacci retracements and extensions, Elliott Wave concepts, and Wyckoff phase identification. It provides traders with a geometric framework for understanding market structure and identifying potential reversal patterns with multi-factor signal confirmation.
What This Indicator Does
The indicator calculates and displays:
Fractal Detection - Identifies fractal highs and lows using Williams-style pivot analysis with configurable period
Fractal Dimension - Calculates market complexity using range-based dimension estimation
Harmonic Patterns - Detects Gartley, Butterfly, Bat, Crab, Shark, Cypher, and ABCD patterns using Fibonacci ratios
Fibonacci Retracements - Key levels at 38.2%, 50%, and 61.8%
Fibonacci Extensions - Projection level at 161.8%
Elliott Wave Count - Simplified wave counting based on pivot detection (1-5)
Wyckoff Phase - Volume-based phase identification (Accumulation, Markup, Distribution, Neutral)
Golden Spiral Levels - ATR-based support and resistance levels using phi (1.618) ratio
Trend Detection - EMA crossover trend identification (20/50 EMA)
How It Works
Fractal detection uses a configurable period to identify swing points:
detectFractalHigh(simple int period) =>
bool result = true
float centerVal = high
for i = 0 to period - 1
if high >= centerVal or high >= centerVal
result := false
break
Harmonic pattern detection uses Fibonacci ratio analysis between swing points. Each pattern has specific ratio requirements:
Gartley: AB 0.382-0.618, BC 0.382-0.886, CD 1.27-1.618
Butterfly: AB 0.382-0.5, BC 0.382-0.886, CD 1.618-2.24
Bat: AB 0.5-0.618, BC 1.13-1.618, CD 1.618-2.24
Crab: AB 0.382-0.618, BC 0.382-0.886, CD 2.24-3.618
Shark: AB 0.382-0.618, BC 1.13-1.618, CD 1.618-2.24
Cypher: AB 0.382-0.618, BC 1.13-1.414, CD 0.786-0.886
Wyckoff phase detection analyzes volume relative to price movement:
wyckoffPhase(simple int period) =>
float avgVol = ta.sma(volume, period)
float priceChg = ta.change(close, period)
string phase = "NEUTRAL"
if volume > avgVol * 1.5 and math.abs(priceChg) < close * 0.02
phase := "ACCUMULATION"
else if volume > avgVol * 1.5 and math.abs(priceChg) > close * 0.05
phase := "MARKUP"
else if volume < avgVol * 0.7
phase := "DISTRIBUTION"
phase
Signal Generation
Signals use multi-factor confirmation for accuracy:
BUY Signal: Fractal low + Uptrend (EMA20 > EMA50) + RSI 30-55 + Bullish candle + Volume confirmation
SELL Signal: Fractal high + Downtrend (EMA20 < EMA50) + RSI 45-70 + Bearish candle + Volume confirmation
Pattern Detection: Label appears when harmonic pattern completes at current bar
Dashboard Panel (Top-Right)
Dimension - Fractal dimension value (market complexity measure)
Last High - Most recent fractal high price
Last Low - Most recent fractal low price
Pattern - Current harmonic pattern name or NONE
Elliott Wave - Current wave count (Wave 1-5) or OFF
Wyckoff - Current market phase or OFF
Trend - BULLISH, BEARISH, or NEUTRAL based on EMA crossover
Signal - BUY, SELL, or WAIT status
Visual Elements
Fractal Markers - Small triangles at fractal highs (down arrow) and lows (up arrow)
Geometry Lines - Dashed lines connecting the most recent fractal high and low
Fibonacci Levels - Clean horizontal lines at 38.2%, 50%, and 61.8% retracement levels
Fibonacci Extension - Horizontal line at 161.8% extension level
Golden Spiral Levels - Support and resistance lines based on ATR x 1.618
3D Fractal Field - Optional depth layers around swing levels (OFF by default)
Harmonic Pattern Markers - Small diamond shapes when Crab, Shark, or Cypher patterns detected
Pattern Labels - Text label showing pattern name when detected
Signal Labels - BUY/SELL labels on confirmed multi-factor signals
Input Parameters
Fractal Period (default: 5) - Bars on each side for fractal detection
Geometry Depth (default: 3) - Complexity of geometric calculations
Pattern Sensitivity (default: 0.8) - Tolerance for pattern ratio matching
Show Fibonacci Levels (default: true) - Display retracement levels
Show Fibonacci Extensions (default: true) - Display extension level
Elliott Wave Detection (default: true) - Enable wave counting
Wyckoff Analysis (default: true) - Enable phase detection
Golden Spiral Levels (default: true) - Display spiral support/resistance
Show Fractal Points (default: true) - Display fractal markers
Show Geometry Lines (default: true) - Display connecting lines
Show Pattern Labels (default: true) - Display pattern name labels
Show 3D Fractal Field (default: false) - Display depth layers
Show Harmonic Patterns (default: true) - Display pattern markers
Show Buy/Sell Signals (default: true) - Display signal labels
Suggested Use Cases
Identify potential reversal zones using harmonic pattern completion
Use Fibonacci levels for entry, stop-loss, and target planning
Monitor Wyckoff phases for accumulation/distribution awareness
Track Elliott Wave counts for trend structure analysis
Use fractal dimension to gauge market complexity
Wait for multi-factor signal confirmation before entering trades
Timeframe Recommendations
Best on 1H to Daily charts. Lower timeframes produce more fractals but with less significance. Higher timeframes provide stronger levels and more reliable signals.
Limitations
Harmonic pattern detection uses simplified ratio ranges and may not match all textbook definitions
Elliott Wave counting is basic and does not include all wave rules
Wyckoff phase detection is volume-based approximation
Fractal dimension calculation is simplified
Signals require fractal confirmation which has inherent lag equal to the fractal period
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management.
- Made with passion by officialjackofalltrades
Indicator
Indicator
Candlestick Pattern Indicator – Doji, Harami, More [algo_aakash]This Candlestick Pattern Indicator is designed to help traders identify key price action patterns like Bullish Engulfing, Bearish Engulfing, Doji, Hammer, Morning Star, Evening Star, and many more directly on your PulseWire chart. With customizable options to display both bullish and bearish patterns , this indicator provides real-time visual markers and labels, helping you make informed trading decisions.
Key features of the indicator include:
Detects popular candlestick patterns such as Bullish Engulfing, Bearish Engulfing, Hammer, Morning Star, Tweezer Tops, and more.
Customizable settings for displaying pattern shapes, labels, and opacity, tailored to your trading preferences.
Option to plot signals only after a candle closes, ensuring accuracy.
Alerts for immediate notification of detected patterns.
Visual markers on the chart, including arrows and labels, for quick recognition of potential trade setups.
This indicator is ideal for traders who rely on candlestick patterns for technical analysis and want an automated tool to highlight these setups for easier decision-making.
Whether you're a beginner or an experienced trader, this tool will help you spot important patterns in real-time without cluttering your chart. Indicator
