Volatility Squeeze Pro [JOAT]

Volatility Squeeze Pro — Advanced Volatility Compression Analysis System

This indicator addresses a specific analytical challenge in volatility analysis: how to identify periods when different volatility measurements show compression relationships that may indicate potential energy buildup in the market. It combines two distinct volatility calculation methods—standard deviation-based bands and ATR-based channels—with a momentum oscillator to provide comprehensive volatility state analysis.

Why This Combination Provides Unique Analytical Value

Traditional volatility indicators typically focus on single measurements, but markets exhibit different types of volatility that require different analytical approaches:

1. **Closing Price Volatility** (Standard Deviation): Measures how much closing prices deviate from their average
2. **Trading Range Volatility** (ATR): Measures the actual high-to-low trading ranges
3. **Directional Momentum**: Measures where price sits within its recent range

The problem with using these individually:
- Standard deviation alone doesn't account for intraday volatility
- ATR alone doesn't consider closing price clustering
- Momentum alone doesn't provide volatility context
- No single measurement captures the complete volatility picture

This indicator's originality lies in creating a comprehensive volatility analysis system that:

**Identifies Volatility Compression**: When closing price volatility contracts inside trading range volatility, it suggests potential energy buildup
**Provides Momentum Context**: Shows directional bias during compression periods
**Offers Multi-Dimensional Analysis**: Combines three different analytical approaches into one coherent system
**Delivers Real-Time Assessment**: Continuously monitors the relationship between different volatility types

Technical Innovation and Originality

While individual components (Bollinger Bands, Keltner Channels, Linear Regression) are standard, the innovation lies in:

1. **Volatility Relationship Detection**: The mathematical comparison between standard deviation bands and ATR channels creates a unique compression identification system

2. **Integrated Momentum Analysis**: Linear regression-based momentum calculation provides directional context specifically during volatility compression periods

3. **Multi-State Visualization**: The indicator provides clear visual encoding of different volatility states (compressed vs. normal) with momentum direction

4. **Adaptive Threshold System**: The squeeze detection automatically adapts to different instruments and timeframes without manual calibration

How the Components Work Together Analytically

The three components create a comprehensive volatility analysis framework:

**Standard Deviation Component**: Measures closing price dispersion around the mean

float bbBasis = ta.sma(close, bbLength)
float bbDev = bbMult * ta.stdev(close, bbLength)
float bbUpper = bbBasis + bbDev
float bbLower = bbBasis - bbDev

**ATR Channel Component**: Measures actual trading range volatility

float kcBasis = ta.ema(close, kcLength)  
float kcRange = ta.atr(atrLength)
float kcUpper = kcBasis + kcRange * kcMult
float kcLower = kcBasis - kcRange * kcMult

**Squeeze Detection Logic**: Identifies when closing price volatility compresses within trading range volatility

bool squeezeOn = bbLower > kcLower and bbUpper < kcUpper
// This condition indicates closing prices are clustering more tightly 
// than the typical trading range would suggest

**Momentum Context Component**: Provides directional bias during compression

float highestHigh = ta.highest(high, momLength)
float lowestLow = ta.lowest(low, momLength)  
float momentum = ta.linreg(close - math.avg(highestHigh, lowestLow), momLength, 0)
float momSmooth = ta.sma(momentum, smoothLength)

The analytical relationship creates a system where:
- Squeeze detection identifies WHEN volatility compression occurs
- Momentum analysis shows WHERE price is positioned during compression
- Combined analysis provides both timing and directional context

How the Volatility Comparison Works

The indicator compares two volatility measurements:

Standard Deviation Bands
These measure how much closing prices deviate from their average. When prices cluster tightly around the average, the bands contract.

// Standard deviation bands calculation
float bbBasis = ta.sma(close, bbLength)
float bbDev = bbMult * ta.stdev(close, bbLength)
float bbUpper = bbBasis + bbDev
float bbLower = bbBasis - bbDev

ATR-Based Channels
These measure volatility using Average True Range—the typical distance between high and low prices. They respond to the actual trading range rather than closing price dispersion.

// ATR-based channels calculation
float kcBasis = ta.ema(close, kcLength)
float kcRange = ta.atr(atrLength)
float kcUpper = kcBasis + kcRange * kcMult
float kcLower = kcBasis - kcRange * kcMult

The Squeeze Condition
A "squeeze" is detected when the standard deviation bands are completely contained within the ATR channels:

// Squeeze detection
bool squeezeOn = bbLower > kcLower and bbUpper < kcUpper

This condition indicates that closing price volatility has compressed relative to the overall trading range.

The Momentum Component

The momentum oscillator measures where price sits relative to its recent high-low range, using linear regression for smoothing:

// Momentum calculation
float highestHigh = ta.highest(high, momLength)
float lowestLow = ta.lowest(low, momLength)
float momentum = ta.linreg(close - math.avg(highestHigh, lowestLow), momLength, 0)
float momSmooth = ta.sma(momentum, smoothLength)

Positive values indicate price is above the midpoint of its recent range; negative values indicate below.

Why Display Both Together

The squeeze detection shows WHEN volatility is compressed. The momentum reading shows the current directional bias of price within that compression. Together, they provide two pieces of information:
1. Is volatility currently compressed? (squeeze status)
2. Where is price leaning within the current range? (momentum)

These are observations about current conditions, not predictions about future movement.

Visual Elements

• Momentum Histogram — Bars showing momentum value
  - Green shades: Positive momentum (price above range midpoint)
  - Red shades: Negative momentum (price below range midpoint)
  - Brighter colors: Momentum increasing
  - Faded colors: Momentum decreasing
  
• Squeeze Dots — Circles on the zero line
  - Red: Squeeze condition active
  - Green: No squeeze condition
  
• Release Markers — Triangle markers when squeeze condition ends
  
• Dashboard — Current readings and status
  

Color Scheme

• Squeeze Active — #FF5252 (red)
  
• No Squeeze — #4CAF50 (green)
  
• Momentum Positive — #00E676 / #81C784 (green shades)
  
• Momentum Negative — #FF5252 / #E57373 (red shades)
  

Inputs

Standard Deviation Bands:

• Length (default: 20)
  
• Multiplier (default: 2.0)
  

ATR Channels:

• Length (default: 20)
  
• Multiplier (default: 1.5)
  
• ATR Period (default: 10)
  

Momentum:

• Length (default: 12)
  
• Smoothing (default: 3)
  

How to Read the Display

• Red dots indicate the squeeze condition is present
  
• Green dots indicate normal volatility relationship
  
• Histogram direction shows current momentum bias
  
• Histogram color brightness shows whether momentum is increasing or decreasing
  

Alerts

• Squeeze condition started
  
• Squeeze condition ended
  
• Squeeze ended with positive momentum
  
• Squeeze ended with negative momentum
  
• Extended squeeze (8+ bars)
  

Important Limitations and Realistic Expectations

• Volatility compression detection is a mathematical relationship between calculations—it does not predict future price movements
  
• Many compression periods do not result in significant price expansion or directional moves
  
• Momentum direction during compression does not reliably indicate future breakout direction
  
• This indicator analyzes current and historical volatility conditions only—it cannot predict future volatility
  
• False signals are common—not every squeeze leads to tradeable price movement
  
• Different parameter settings will produce different compression detection sensitivity
  
• Market conditions, news events, and fundamental factors often override technical volatility patterns
  
• No volatility indicator can predict the timing, direction, or magnitude of future price movements
  
• This tool should be used as one component of comprehensive market analysis
  

Appropriate Use Cases

This indicator is designed for:
- Volatility state analysis and monitoring
- Educational study of volatility relationships
- Multi-dimensional volatility assessment
- Supplementary analysis alongside other technical tools
- Understanding market compression/expansion cycles

This indicator is NOT designed for:
- Standalone trading signal generation
- Guaranteed breakout prediction
- Automated trading system triggers
- Market timing precision
- Replacement of fundamental analysis

Understanding Volatility Analysis Limitations

Volatility analysis, while useful for understanding market conditions, has inherent limitations:
- Past volatility patterns do not guarantee future patterns
- Compression periods can extend much longer than expected
- Expansion periods may be brief and insufficient for trading
- External factors (news, fundamentals) often override technical patterns
- Different markets and timeframes exhibit different volatility characteristics

— Made with passion by officialjackofalltrades