Bollinger Bands Squeeze📈 Bollinger Bands Squeeze
This indicator enhances traditional Bollinger Bands by integrating Keltner Channel layers to visualize market compression and volatility expansion — allowing traders to easily identify when a squeeze is building or releasing.
🔍 Overview
This is a refined version of the classic Bollinger Bands, designed to detect volatility squeezes using multiple Keltner Channel thresholds.
The script plots standard Bollinger Bands and dynamically colors the bands according to the degree of compression relative to the Keltner Channels.
⚙️ How It Works
Bollinger Bands are calculated from a selected moving average (SMA, EMA, SMMA, WMA, or VWMA) and standard deviation multiplier.
Keltner Channels are derived from ATR (True Range) using three sensitivity levels (1.0, 1.5, and 2.0× multipliers).
When Bollinger Bands contract inside a Keltner Channel, the script marks a squeeze state:
🟠 High Compression (Orange): Very tight volatility — expect breakout soon.
🔴 Mid Compression (Red): Moderate contraction — volatility is building.
⚫ Low Compression (Gray/Black): Early compression phase.
🧩 Inputs & Customization
Length : Period for both Bollinger and Keltner calculations.
Basis MA Type: Choose from SMA, EMA, SMMA (RMA), WMA, or VWMA.
StdDev Multiplier : Controls Bollinger Bandwidth.
Keltner Multipliers (1.0 / 1.5 / 2.0) : Adjust compression thresholds.
Offset : Shifts the bands visually on the chart.
🕹️ Best Use Cases
Identify pre-breakout conditions before volatility expansion.
Combine with volume, momentum, or trend indicators (e.g., RSI) for confirmation.
Ideal for scalping, breakout trading, or volatility-based entries during session opens.
在脚本中搜索"Volatility"
Volatility Channel Oscillator█ OVERVIEW
"Volatility Channel Oscillator" is a technical indicator that analyzes price volatility relative to dynamic price channels, displaying an oscillator, its moving average, and signals based on crossovers and divergences. The indicator offers customizable overbought and oversold levels, gradient visualization, and divergence detection, supported by alerts for key signals.
█ CONCEPTS
The VCO indicator creates dynamic price channels based on a moving average of the price (calculated as the arithmetic mean of the high and low prices: (high + low) / 2) and market volatility (measured as the average candle range and body size). These channels are not displayed on the chart but are used to calculate the oscillator value, which reflects the position of the closing price relative to the channel width, scaled to a range from -100 to +100, with the zero line as the central point. A moving average of the oscillator (SMA) smooths its values, enabling signals based on crossovers with the zero line or overbought/oversold levels. The indicator also detects divergences between price and the oscillator, which may indicate potential trend reversals. VCO is useful for identifying market momentum, reversal points, and trend confirmation, especially when combined with other technical analysis tools.
█ FEATURES
- Volatility Channels: Calculates invisible chart boundaries based on a simple moving average (SMA) of the price (high + low) / 2 and volatility (average candle range and body). The length parameter (default 30) sets the SMA length, and scale (default 200%) adjusts the channel width.
- Oscillator: Determines the oscillator value in the range of -100 to +100, indicating the closing price's position relative to the volatility channel. Displayed with dynamic coloring (green for positive values, red for negative).
- Oscillator Moving Average: A simple moving average (SMA) of the oscillator values, smoothing its movements. The signalLength parameter (default 20) defines the SMA length. Displayed in yellow with an optional gradient.
- Overbought/Oversold Levels: Configurable thresholds for the oscillator (overbought, default 50; oversold, default -50) and its moving average (maOverbought, default 30; maOversold, default -30), shown as horizontal lines with optional gradients. Band colors change dynamically (red for overbought, green for oversold, gray for neutral) based on the moving average's position relative to maOverbought/maOversold, reinforcing other signals.
- Divergences: Detects bullish (price forms a lower low, oscillator a higher low) and bearish (price forms a higher high, oscillator a lower high) divergences using pivots (pivotLength, default 2). Divergences are displayed with a delay equal to the pivot length; larger lengths increase reliability but delay signals. Use as additional confirmation.
Signals:
- Overbought/Oversold Crossovers: Green triangles (buy) when the oscillator crosses above the oversold level, red triangles (sell) when it crosses below the overbought level.
- Zero Line Crossovers: Buy/sell signals when the oscillator crosses the zero line upward (buy) or downward (sell).
- Moving Average Crossovers: Buy/sell signals when the oscillator's moving average crosses the zero line or the maOverbought/maOversold levels. Dynamic band color changes (red/green) at these crossovers reinforce other signals.
- Visualization: Gradient lines for the oscillator, its moving average, overbought/oversold levels, and zero line, with adjustable transparency. Gradient fill between the oscillator and zero line.
Divergence Labels: "Bull" (bullish) and "Bear" (bearish) labels with customizable color and transparency.
- Alerts: Built-in alerts for divergences, overbought/oversold crossovers, and zero line crossovers by the oscillator and its moving average.
█ HOW TO USE
Add to Chart: Apply the indicator via Pine Editor or the Indicators menu on TradingView.
Configure Settings:
- Channel and Oscillator Settings: Adjust the channel SMA length (length, default 30) and channel scaling (scale, default 200%). Increase scale for high-volatility markets.
- Threshold Levels: Set oscillator overbought (overbought, default 50) and oversold (oversold, default -50) levels, and moving average thresholds (maOverbought, default 30; maOversold, default -30).
- Divergence Settings: Enable/disable divergence detection (calculateDivergence) and set pivot length (pivotLength, default 2). Larger values increase reliability but delay signals.
- Signal Settings: Choose signal types (signalType): overbought/oversold, zero line, moving average, or all.
- Styling: Customize colors for the oscillator, moving average, horizontal levels, and divergence labels. Adjust gradient and fill transparency.
Interpreting Signals:
- Buy Signals: Green triangles below the bar when the oscillator or its moving average crosses above the oversold level or zero line.
- Sell Signals: Red triangles above the bar when the oscillator or its moving average crosses below the overbought level or zero line.
- Moving Average Signals: Green/red triangles when the moving average crosses maOverbought/maOversold levels, indicating potential reversals or trend continuation. Dynamic band color changes (red for overbought, green for oversold) at these crossovers reinforce other signals.
- Divergences: "Bull" (bullish) and "Bear" (bearish) labels indicate potential trend reversals with a delay based on pivot length. Use as confirmation.
- Overbought/Oversold Levels: Monitor price reactions in these zones as potential reversal points. Dynamic band color changes based on the moving average reinforce signals.
Signal Confirmation: Use VCO with other tools, such as pivot levels (for key turning points) or Fibonacci levels (for support/resistance zones).
█ APPLICATIONS
- Trend Trading: Zero line crossovers by the oscillator or its moving average identify momentum in uptrends or downtrends.
- Range Trading: Overbought/oversold levels help identify entry/exit points in sideways markets.
- Divergences: Use bullish/bearish divergences as additional confirmation of reversals, especially near key price levels.
- Trend Identification: To analyze trends over a longer perspective, increase the moving average length (signalLength) for more stable signals.
█ NOTES
- Test the indicator across different timeframes and markets to optimize parameters, such as length and scale, for your trading style.
- In strong trends, overbought/oversold levels may persist, requiring additional signal verification.
- Divergences are more reliable on higher timeframes (H4, D1), where market noise is reduced, but their delay requires caution.
- In low-liquidity markets, signals may be less effective, so use on high-liquidity assets is recommended.
Japan Yen Carry Trade to Risk Ratio Sharpe Ratio By UncleBFMStep-by-Step Calculation in the ScriptFetch Rates:Pulls rates dynamically using request.security() from user-specified symbols (e.g., TVC:JP10Y for yen, TVC:US10Y for target). If unavailable (NA), uses fallback inputs (e.g., 0.25% for yen, 4.50% for target).
Converts rates to decimals: (target_rate - yen_rate) / 100.
Calculate Carry:Carry = (Target Rate - Yen Rate) / 100
Example: If US 10Y yield is 4.50% and Japan 10Y is 0.25%, carry = (4.50 - 0.25) / 100 = 0.0425 (4.25% annual yield).
Calculate Daily Log Returns:Log Returns = ln(Close / Close ), where Close is the current price of the pair (e.g., USDJPY) and Close is the previous day's price.
This measures daily percentage changes in a way suitable for volatility calculations.
Calculate Annualized Volatility:Volatility = Standard Deviation of Log Returns over a lookback period (default 63 days, ~3 months) × √252.
Example: If the standard deviation of USDJPY log returns is 0.005 (0.5% daily), annualized volatility = 0.005 × √252 ≈ 0.0794 (7.94%).
Compute the Ratio:Ratio = Carry / Volatility
Example: Using above, 0.0425 / 0.0794 ≈ 0.535.
If volatility is zero, the ratio is set to NA to avoid division errors.
Plot:Plots the ratio as a line, with optional thresholds (e.g., 0.2 for "high attractiveness") to guide interpretation.
NotesDynamic Rates: Using bond yields (e.g., TVC:JP10Y) or policy rates (e.g., ECONOMICS:JPINTR) makes the indicator responsive to historical and current rate changes, unlike static inputs.
Context: BIS reports use similar ratios to assess carry trade viability. For USDJPY in 2025, with Fed rates around 4.5% and BoJ at 0.25–0.5%, the carry is positive but sensitive to volatility spikes (e.g., during 2024 unwind events).
Usage: Apply to a yen pair chart (e.g., USDJPY, AUDJPY). Adjust symbols for the target currency (e.g., TVC:AU10Y for AUD). The ratio helps compare carry trade profitability across pairs or over time.
Candle Spread + ATR SMA Analysis
This indicator combines elements from two popular open-source scripts — Candle Range Compare
by @oldinvestor
and Objective Analysis of Spread (VSA)
by @Rin-Nin
— into a single tool for analyzing candle spreads (ranges and bodies) in relation to volatility benchmarks.
🔎 What It Does
Candle Decomposition:
Plots total candle ranges (high–low) in gray, for both up and down closes.
Plots up-close bodies (open–close) in white.
Plots down-close bodies in black.
This makes it easy to spot whether volatility comes from real price movement (body) or extended wicks.
ATR & SMA Volatility Bands:
Calculates ATR (Average True Range) and overlays it as a black line.
Plots four volatility envelopes derived from the SMA of the true range:
0.8× (blue, shaded)
1.3× (green)
1.8× (red)
3.0× (purple)
Colored fill zones highlight when candle spreads are below, within, or above key thresholds.
Visual Context:
Track expansion/contraction in spreads.
Compare bullish (white) vs bearish (black) bodies to gauge buying/selling pressure.
Identify when candles stretch beyond typical volatility ranges.
📈 How To Use It
VSA context: Wide down bars (black) beyond ATR bands may suggest supply; wide up bars (white) may indicate demand.
Trend confirmation: Expanding ranges above average thresholds (green/red/purple bands) often confirm momentum.
Reversal potential: Small bodies but large ranges (gray + wicks) frequently appear at turning points.
Volatility filter: Use ATR bands to filter trades — e.g., only act when candle ranges exceed 1.3× or 1.8× SMA thresholds.
🙏 Credits
This script is inspired by and combines ideas from:
Candle Range Compare
by @oldinvestor
Objective Analysis of Spread (VSA)
by @Rin-Nin
Big thanks to both authors for their valuable contributions to the TradingView community.
One thing I couldnt quite get to work is being able to display up and down wicks like in the candle range compare, so I just add that indicator to the chart as well, uncheck everything but the wick plots and there it is.
IV PercentileIV Percentile Indicator - Brief Description
What It Does
The IV Percentile Indicator measures where current implied volatility ranks compared to the past year, showing what percentage of time volatility was lower than today's level.
How It Works
Data Collection:
Tracks implied volatility (or historical volatility as proxy) for each trading day
Stores the last 252 days (1 year) of volatility readings
Uses VIX data for SPY/SPX, historical volatility for other stocks
Calculation:
IV Percentile = (Days with IV below current level) ÷ (Total days) × 100
Example: If IV Percentile = 75%, it means current volatility is higher than 75% of the past year's readings.
Visual Output
Main Display:
Blue line showing percentile (0-100%)
Reference lines at key levels (20%, 30%, 50%, 70%, 80%)
Color-coded backgrounds for quick identification
Info table with current readings
Key Levels:
80%+ (Red): Very high IV → Sell premium
70-79% (Orange): High IV → Consider selling
30-20% (Green): Low IV → Consider buying
<20% (Bright Green): Very low IV → Buy premium
Trading Application
When IV Percentile is HIGH (70%+):
Options are expensive relative to recent history
Good time to sell premium (iron condors, credit spreads)
Expect volatility to decrease toward normal levels
When IV Percentile is LOW (30%-):
Options are cheap relative to recent history
Good time to buy premium (straddles, long options)
Expect volatility to increase from compressed levels
Core Logic
The indicator helps answer: "Is this a good time to buy or sell options based on how expensive/cheap they are compared to recent history?" It removes the guesswork from volatility timing by providing historical context for current option prices.
Topological Market Stress (TMS) - Quantum FabricTopological Market Stress (TMS) - Quantum Fabric
What Stresses The Market?
Topological Market Stress (TMS) represents a revolutionary fusion of algebraic topology and quantum field theory applied to financial markets. Unlike traditional indicators that analyze price movements linearly, TMS examines the underlying topological structure of market data—detecting when the very fabric of market relationships begins to tear, warp, or collapse.
Drawing inspiration from the ethereal beauty of quantum field visualizations and the mathematical elegance of topological spaces, this indicator transforms complex mathematical concepts into an intuitive, visually stunning interface that reveals hidden market dynamics invisible to conventional analysis.
Theoretical Foundation: Topology Meets Markets
Topological Holes in Market Structure
In algebraic topology, a "hole" represents a fundamental structural break—a place where the normal connectivity of space fails. In markets, these topological holes manifest as:
Correlation Breakdown: When traditional price-volume relationships collapse
Volatility Clustering Failure: When volatility patterns lose their predictive power
Microstructure Stress: When market efficiency mechanisms begin to fail
The Mathematics of Market Topology
TMS constructs a topological space from market data using three key components:
1. Correlation Topology
ρ(P,V) = correlation(price, volume, period)
Hole Formation = 1 - |ρ(P,V)|
When price and volume decorrelate, topological holes begin forming.
2. Volatility Clustering Topology
σ(t) = volatility at time t
Clustering = correlation(σ(t), σ(t-1), period)
Breakdown = 1 - |Clustering|
Volatility clustering breakdown indicates structural instability.
3. Market Efficiency Topology
Efficiency = |price - EMA(price)| / ATR
Measures how far price deviates from its efficient trajectory.
Multi-Scale Topological Analysis
Markets exist across multiple temporal scales simultaneously. TMS analyzes topology at three distinct scales:
Micro Scale (3-15 periods): Immediate structural changes, market microstructure stress
Meso Scale (10-50 periods): Trend-level topology, medium-term structural shifts
Macro Scale (50-200 periods): Long-term structural topology, regime-level changes
The final stress metric combines all scales:
Combined Stress = 0.3×Micro + 0.4×Meso + 0.3×Macro
How TMS Works
1. Topological Space Construction
Each market moment is embedded in a multi-dimensional topological space where:
- Price efficiency forms one dimension
- Correlation breakdown forms another
- Volatility clustering breakdown forms the third
2. Hole Detection Algorithm
The indicator continuously scans this topological space for:
Hole Formation: When stress exceeds the formation threshold
Hole Persistence: How long structural breaks maintain
Hole Collapse: Sudden topology restoration (regime shifts)
3. Quantum Visualization Engine
The visualization system translates topological mathematics into intuitive quantum field representations:
Stress Waves: Main line showing topological stress intensity
Quantum Glow: Surrounding field indicating stress energy
Fabric Integrity: Background showing structural health
Multi-Scale Rings: Orbital representations of different timeframes
4. Signal Generation
Stable Topology (✨): Normal market structure, standard trading conditions
Stressed Topology (⚡): Increased structural tension, heightened volatility expected
Topological Collapse (🕳️): Major structural break, regime shift in progress
Critical Stress (🌋): Extreme conditions, maximum caution required
Inputs & Parameters
🕳️ Topological Parameters
Analysis Window (20-200, default: 50)
Primary period for topological analysis
20-30: High-frequency scalping, rapid structure detection
50: Balanced approach, recommended for most markets
100-200: Long-term position trading, major structural shifts only
Hole Formation Threshold (0.1-0.9, default: 0.3)
Sensitivity for detecting topological holes
0.1-0.2: Very sensitive, detects minor structural stress
0.3: Balanced, optimal for most market conditions
0.5-0.9: Conservative, only major structural breaks
Density Calculation Radius (0.1-2.0, default: 0.5)
Radius for local density estimation in topological space
0.1-0.3: Fine-grained analysis, sensitive to local changes
0.5: Standard approach, balanced sensitivity
1.0-2.0: Broad analysis, focuses on major structural features
Collapse Detection (0.5-0.95, default: 0.7)
Threshold for detecting sudden topology restoration
0.5-0.6: Very sensitive to regime changes
0.7: Balanced, reliable collapse detection
0.8-0.95: Conservative, only major regime shifts
📊 Multi-Scale Analysis
Enable Multi-Scale (default: true)
- Analyzes topology across multiple timeframes simultaneously
- Provides deeper insight into market structure at different scales
- Essential for understanding cross-timeframe topology interactions
Micro Scale Period (3-15, default: 5)
Fast scale for immediate topology changes
3-5: Ultra-fast, tick/minute data analysis
5-8: Fast, 5m-15m chart optimization
10-15: Medium-fast, 30m-1H chart focus
Meso Scale Period (10-50, default: 20)
Medium scale for trend topology analysis
10-15: Short trend structures
20-25: Medium trend structures (recommended)
30-50: Long trend structures
Macro Scale Period (50-200, default: 100)
Slow scale for structural topology
50-75: Medium-term structural analysis
100: Long-term structure (recommended)
150-200: Very long-term structural patterns
⚙️ Signal Processing
Smoothing Method (SMA/EMA/RMA/WMA, default: EMA) Method for smoothing stress signals
SMA: Simple average, stable but slower
EMA: Exponential, responsive and recommended
RMA: Running average, very smooth
WMA: Weighted average, balanced approach
Smoothing Period (1-10, default: 3)
Period for signal smoothing
1-2: Minimal smoothing, noisy but fast
3-5: Balanced, recommended for most applications
6-10: Heavy smoothing, slow but very stable
Normalization (Fixed/Adaptive/Rolling, default: Adaptive)
Method for normalizing stress values
Fixed: Static 0-1 range normalization
Adaptive: Dynamic range adjustment (recommended)
Rolling: Rolling window normalization
🎨 Quantum Visualization
Fabric Style Options:
Quantum Field: Flowing energy visualization with smooth gradients
Topological Mesh: Mathematical topology with stepped lines
Phase Space: Dynamical systems view with circular markers
Minimal: Clean, simple display with reduced visual elements
Color Scheme Options:
Quantum Gradient: Deep space blue → Quantum red progression
Thermal: Black → Hot orange thermal imaging style
Spectral: Purple → Gold full spectrum colors
Monochrome: Dark gray → Light gray elegant simplicity
Multi-Scale Rings (default: true)
- Display orbital rings for different time scales
- Visualizes how topology changes across timeframes
- Provides immediate visual feedback on cross-scale dynamics
Glow Intensity (0.0-1.0, default: 0.6)
Controls the quantum glow effect intensity
0.0: No glow, pure line display
0.6: Balanced, recommended setting
1.0: Maximum glow, full quantum field effect
📋 Dashboard & Alerts
Show Dashboard (default: true)
Real-time topology status display
Current market state and trading recommendations
Stress level visualization and fabric integrity status
Show Theory Guide (default: true)
Educational panel explaining topological concepts
Dashboard interpretation guide
Trading strategy recommendations
Enable Alerts (default: true)
Extreme stress detection alerts
Topological collapse notifications
Hole formation and recovery signals
Visual Logic & Interpretation
Main Visualization Elements
Quantum Stress Line
Primary indicator showing topological stress intensity
Color intensity reflects current market state
Line style varies based on selected fabric style
Glow effect indicates stress energy field
Equilibrium Line
Silver line showing average stress level
Reference point for normal market conditions
Helps identify when stress is elevated or suppressed
Upper/Lower Bounds
Red upper bound: High stress threshold
Green lower bound: Low stress threshold
Quantum fabric fill between bounds shows stress field
Multi-Scale Rings
Aqua circles : Micro-scale topology (immediate changes)
Orange circles: Meso-scale topology (trend-level changes)
Provides cross-timeframe topology visualization
Dashboard Information
Topology State Icons:
✨ STABLE: Normal market structure, standard trading conditions
⚡ STRESSED: Increased structural tension, monitor closely
🕳️ COLLAPSE: Major structural break, regime shift occurring
🌋 CRITICAL: Extreme conditions, reduce risk exposure
Stress Bar Visualization:
Visual representation of current stress level (0-100%)
Color-coded based on current topology state
Real-time percentage display
Fabric Integrity Dots:
●●●●● Intact: Strong market structure (0-30% stress)
●●●○○ Stressed: Weakening structure (30-70% stress)
●○○○○ Fractured: Breaking down structure (70-100% stress)
Action Recommendations:
✅ TRADE: Normal conditions, standard strategies apply
⚠️ WATCH: Monitor closely, increased vigilance required
🔄 ADAPT: Change strategy, regime shift in progress
🛑 REDUCE: Lower risk exposure, extreme conditions
Trading Strategies
In Stable Topology (✨ STABLE)
- Normal trading conditions apply
- Use standard technical analysis
- Regular position sizing appropriate
- Both trend-following and mean-reversion strategies viable
In Stressed Topology (⚡ STRESSED)
- Increased volatility expected
- Widen stop losses to account for higher volatility
- Reduce position sizes slightly
- Focus on high-probability setups
- Monitor for potential regime change
During Topological Collapse (🕳️ COLLAPSE)
- Major regime shift in progress
- Adapt strategy immediately to new market character
- Consider closing positions that rely on previous regime
- Wait for new topology to stabilize before major trades
- Opportunity for contrarian plays if collapse is extreme
In Critical Stress (🌋 CRITICAL)
- Extreme market conditions
- Significantly reduce risk exposure
- Avoid new positions until stress subsides
- Focus on capital preservation
- Consider hedging existing positions
Advanced Techniques
Multi-Timeframe Topology Analysis
- Use higher timeframe TMS for regime context
- Use lower timeframe TMS for precise entry timing
- Alignment across timeframes = highest probability trades
Topology Divergence Trading
- Most powerful at regime boundaries
- Price makes new high/low but topology stress decreases
- Early warning of potential reversals
- Combine with key support/resistance levels
Stress Persistence Analysis
- Long periods of stable topology often precede major moves
- Extended stress periods often resolve in regime changes
- Use persistence tracking for position sizing decisions
Originality & Innovation
TMS represents a genuine breakthrough in applying advanced mathematics to market analysis:
True Topological Analysis: Not a simplified proxy but actual topological space construction and hole detection using correlation breakdown, volatility clustering analysis, and market efficiency measurement.
Quantum Aesthetic: Transforms complex topology mathematics into an intuitive, visually stunning interface inspired by quantum field theory visualizations.
Multi-Scale Architecture: Simultaneous analysis across micro, meso, and macro timeframes provides unprecedented insight into market structure dynamics.
Regime Detection: Identifies fundamental market character changes before they become obvious in price action, providing early warning of structural shifts.
Practical Application: Clear, actionable signals derived from advanced mathematical concepts, making theoretical topology accessible to practical traders.
This is not a combination of existing indicators or a cosmetic enhancement of standard tools. It represents a fundamental reimagining of how we measure, visualize, and interpret market dynamics through the lens of algebraic topology and quantum field theory.
Best Practices
Start with defaults: Parameters are optimized for broad market applicability
Match timeframe: Adjust scales based on your trading timeframe
Confirm with price action: TMS shows market character, not direction
Respect topology changes: Reduce risk during regime transitions
Use appropriate strategies: Adapt approach based on current topology state
Monitor persistence: Track how long topology states maintain
Cross-timeframe analysis: Align multiple timeframes for highest probability trades
Alerts Available
Extreme Topological Stress: Market fabric under severe deformation
Topological Collapse Detected: Regime shift in progress
Topological Hole Forming: Market structure breakdown detected
Topology Stabilizing: Market structure recovering to normal
Chart Requirements
Recommended Markets: All liquid markets (forex, stocks, crypto, futures)
Optimal Timeframes: 5m to Daily (adaptable to any timeframe)
Minimum History: 200 bars for proper topology construction
Best Performance: Markets with clear regime characteristics
Academic Foundation
This indicator draws from cutting-edge research in:
- Algebraic topology and persistent homology
- Quantum field theory visualization techniques
- Market microstructure analysis
- Multi-scale dynamical systems theory
- Correlation topology and network analysis
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or provide direct buy/sell signals. Topological analysis reveals market structure characteristics, not future price direction. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results.
See markets through the lens of topology. Trade the structure, not the noise.
Bringing advanced mathematics to practical trading through quantum-inspired visualization.
Trade with insight. Trade with structure.
— Dskyz , for DAFE Trading Systems
EWMA & EWVar + EWStd Expansion with MTF_V.5EWMA & EWVar + EWStd Expansion with MTF_V.5
This indicator combines adaptive trend smoothing (EWMA), variance estimation (EWVar) and dynamic volatility “bursts” (EWStd Expansion) with optional higher-timeframe confirmation. It’s designed both for visual chart analysis and for automated alerts on regime changes.
Key Features
EWMA (Exponential Smoothing):
• Computes an exponential moving average with either a custom α or a length-derived α = 2/(N+1).
• Option to recalculate only every N bars (reduces CPU load).
EWVar & EWStd (Variance & Standard Deviation):
• Exponentially weighted variance tracks recent price dispersion.
• EWStd (σ) is computed alongside the EWMA.
• Z-score (deviation in σ units) shows how far price has diverged from trend.
Multi-Timeframe Filter (MTF):
• Optionally require the same trend direction on a chosen higher timeframe (e.g. Daily, Weekly, H4).
• Real-time lookahead available (may repaint).
Gradient Around EWMA:
• A multi-layer “glow” zone of ±1σ, broken into up to 10 steps.
• Color interpolates between “upper” and “lower” shades for bullish, bearish and neutral regimes.
Instantaneous Trendline (ITL):
• Ultra-fast trend filter with slope-based coloring.
• Highlights micro-trends and short-lived accelerations.
Cross-Over Signals (ITL ↔ EWMA):
• Up/down triangles plotted when the ITL crosses the main EWMA.
EWStd Expansion (Volatility Bursts):
• Automatically detects σ expansions (σ growth above a set % threshold).
• Price filter: only when price moves beyond EWMA ± (multiplier·σ).
• Optional higher-timeframe confirmation.
Labels & Alerts:
• Text labels and circular markers on bars where a volatility burst occurs.
• Built-in alertcondition calls for both bullish and bearish expansions.
How to Use
Visual Analysis:
• The gradient around EWMA shows the width of the volatility channel expanding or contracting.
• ITL color changes instantly highlight short-term impulses.
• EWMA line color switches (bullish/bearish/neutral) indicate trend state.
Spotting Volatility Breakouts:
• “EWStd Expansion” labels and circles signal the onset of strong moves when σ spikes.
• Useful for entering at the start of new impulses.
Automated Alerts:
• Set alerts on the built-in conditions “Bullish EWStd Expansion Alert” or “Bearish EWStd Expansion Alert” to receive a popup or mobile push when a burst occurs.
This compact tool unifies trend, volatility and multi-timeframe analysis into a single indicator—ideal for traders who want to see trend direction, current dispersion, and timely volatility burst signals all at once.
Macd, Wt Cross & HVPMacd Wt Cross & HVP – Advanced Multi-Signal Indicator
This script is a custom-designed multi-signal indicator that brings together three proven concepts to provide a complete view of market momentum, reversals, and volatility build-ups. It is built for traders who want to anticipate key market moves, not just react to them.
Why This Combination ?
While each tool has its strengths, their combined use creates powerful signal confluence.
Instead of juggling multiple indicators separately, this script synchronizes three key perspectives into a single, intuitive display—helping you trade with greater clarity and confidence.
1. MACD Histogram – Momentum and Trend Clarity
At the core of the indicator is the MACD histogram, calculated as the difference between two exponential moving averages (EMAs).
Color-coded bars represent momentum direction and intensity:
Green / blue bars: bullish momentum
Red / pink bars: bearish momentum
Color intensity shows acceleration or weakening of trend.
This visual makes it easy to detect trend shifts and momentum divergence at a glance.
2. WT Cross Signals – Early Reversal Detection
Overlaid on the histogram are green and red dots, based on the logic of the WaveTrend oscillator cross:
Green dots = potential bullish cross (buy signal)
Red dots = potential bearish cross (sell signal)
These signals are helpful for identifying reversal points during both trending and ranging phases.
3. Historical Volatility Percentile (HVP) – Volatility Compression Zones
Behind the histogram, purple vertical zones highlight periods of low historical volatility, based on the HVP:
When volatility compresses below a specific threshold, these zones appear.
Such periods are often followed by explosive price moves, making them prime areas for pre-breakout positioning.
By integrating HVP, the script doesn’t just tell you where the trend is—it tells you when the trend is likely to erupt.
How to Use This Script
Use the MACD histogram to confirm the dominant trend and its strength.
Watch for WT Cross dots as potential entry/exit signals in alignment or divergence with the MACD.
Monitor HVP purple zones as warnings of incoming volatility expansions—ideal moments to prepare for breakout trades.
Best results occur when all three elements align, offering a high-probability trade setup.
What Makes This Script Original?
Unlike many mashups, this script was not created by simply merging indicators. Each component was carefully integrated to serve a specific, complementary purpose:
MACD detects directional bias
WT Cross adds precision timing
HVP anticipates volatility-based breakout timing
This results in a strategic tool for traders, useful on multiple timeframes and adaptable to different trading styles (trend-following, breakout, swing).
Combined ATR + VolumeOverview
The Combined ATR + Volume indicator (C-ATR+Vol) is designed to measure both price volatility and market participation by merging the Average True Range (ATR) and trading volume into a single normalized value. This provides traders with a more comprehensive tool than ATR alone, as it highlights not only how much price is moving, but also whether there is sufficient volume behind those moves.
Originality & Utility
Two Key Components
ATR (Average True Range): Measures price volatility by analyzing the range (high–low) over a specified period. A higher ATR often indicates larger price swings.
Volume: Reflects how actively traders are participating in the market. High volume typically indicates strong buying or selling interest.
Normalized Combination
Both ATR and volume are independently normalized to a 0–100 range.
The final output (C-ATR+Vol) is the average of these two normalized values. This makes it easy to see when both volatility and market participation are relatively high.
Practical Use
Above 80: Signifies elevated volatility and strong volume. Markets may experience significant moves.
Around 50–80: Indicates moderate activity. Price swings and volume are neither extreme nor minimal.
Below 50: Suggests relatively low volatility and lower participation. The market may be ranging or consolidating.
This combined approach can help filter out situations where volatility is high but volume is absent—or vice versa—providing a more reliable context for potential breakouts or trend continuations.
Indicator Logic
ATR Calculation
Uses Pine Script’s built-in ta.tr(true) function to measure true range, then smooths it with a user-selected method (RMA, SMA, EMA, or WMA).
Key Input: ATR Length (default 14).
Volume Calculation
Smooths the built-in volume variable using the same selectable smoothing methods.
Key Input: Volume Length (default 14).
Normalization
For each metric (ATR and Volume), the script finds the lowest and highest values over the lookback period and converts them into a 0–100 scale:
normalized value
=(current value−min)(max−min)×100
normalized value= (max−min)(current value−min) ×100
Combined Score
The final plot is the average of Normalized ATR and Normalized Volume. This single value simplifies the process of identifying high-volatility, high-volume conditions.
How to Use
Setup
Add the indicator to your chart.
Adjust ATR Length, Volume Length, and Smoothing to match your preferred time horizon or chart style.
Interpretation
High Values (above 80): The market is experiencing significant price movement with high participation. Potential for strong trends or breakouts.
Moderate Range (50–80): Conditions are active but not extreme. Trend setups may be forming.
Low Values (below 50): Indicates quieter markets with reduced liquidity. Expect ranging or less decisive moves.
Strategy Integration
Use C-ATR+Vol alongside other trend or momentum indicators (e.g., Moving Averages, RSI, MACD) to confirm potential entries/exits.
Combine it with support/resistance or price action analysis for a broader market view.
Important Notes
This script is open-source and intended as a community contribution.
No Future Guarantee: Past market behavior does not guarantee future results. Always use proper risk management and validate signals with additional tools.
The indicator’s performance may vary depending on timeframes, asset classes, and market conditions.
Adjust inputs as needed to suit different instruments or personal trading styles.
By adhering to TradingView’s publishing rules, this script is provided with sufficient detail on what it does, how it’s unique, and how traders can use it. Feel free to customize the settings and experiment with other technical indicators to develop a trading methodology that fits your objectives.
🔹 Combined ATR + Volume (C-ATR+Vol) 지표 설명
이 인디케이터는 ATR(Average True Range)와 거래량(Volume)을 결합하여 시장의 변동성과 유동성을 동시에 측정하는 지표입니다.
ATR은 가격 변동성의 크기를 나타내며, 거래량은 시장 참여자의 활동 수준을 반영합니다. 보통 높은 ATR은 가격 변동이 크다는 의미이고, 높은 거래량은 시장에서 적극적인 거래가 이루어지고 있음을 나타냅니다.
이 두 지표를 각각 0~100 범위로 정규화한 후, 평균을 구하여 "Combined ATR + Volume (C-ATR+Vol)" 값을 계산합니다.
이를 통해 단순한 가격 변동성뿐만 아니라 거래량까지 고려하여, 더욱 신뢰성 있는 변동성 판단을 할 수 있도록 도와줍니다.
📌 핵심 개념
1️⃣ ATR (Average True Range)란?
시장의 변동성을 측정하는 지표로, 일정 기간 동안의 고점-저점 변동폭을 기반으로 계산됩니다.
ATR이 높을수록 가격 변동이 크며, 낮을수록 횡보장이 지속될 가능성이 큽니다.
하지만 ATR은 방향성을 제공하지 않으며, 단순히 변동성의 크기만을 나타냅니다.
2️⃣ 거래량 (Volume)의 역할
거래량은 시장 참여자의 관심과 유동성을 반영하는 중요한 요소입니다.
높은 거래량은 강한 매수 또는 매도세가 존재함을 의미하며, 낮은 거래량은 시장 참여가 적거나 관심이 줄어들었음을 나타냅니다.
3️⃣ ATR + 거래량의 결합 (C-ATR+Vol)
단순한 ATR 값만으로는 변동성이 커도 거래량이 부족할 수 있으며, 반대로 거래량이 많아도 변동성이 낮을 수 있습니다.
이를 해결하기 위해 ATR과 거래량을 각각 0~100으로 정규화하여 균형 잡힌 변동성 지표를 만들었습니다.
두 지표의 평균값을 계산하여, 가격 변동과 거래량이 동시에 높은지를 측정할 수 있도록 설계되었습니다.
📊 사용법 및 해석
80 이상 → 강한 변동성 구간
가격 변동성이 크고 거래량도 높은 상태
강한 추세가 진행 중이거나 큰 변동이 일어날 가능성이 큼
상승/하락 방향성을 확인한 후 트렌드를 따라가는 전략이 유리
50~80 구간 → 보통 수준의 변동성
가격 움직임이 일정하며, 거래량도 적절한 수준
점진적인 추세 형성이 이루어질 가능성이 있음
시장이 점진적으로 상승 혹은 하락할 가능성이 크므로, 보조지표를 활용하여 매매 타이밍을 결정하는 것이 중요
50 이하 → 낮은 변동성 및 유동성 부족
가격 변동이 적고, 거래량도 낮은 상태
시장이 횡보하거나 조정 기간에 들어갈 가능성이 큼
박스권 매매(지지/저항 활용) 또는 돌파 전략을 고려할 수 있음
💡 활용 방법 및 전략
✅ 1. 트렌드 판단 보조지표로 활용
단독으로 사용하는 것보다는 RSI, MACD, 이동평균선(MA) 등의 지표와 함께 활용하는 것이 효과적입니다.
예를 들어, MACD가 상승 신호를 주고, C-ATR+Vol 값이 80을 초과하면 강한 상승 추세로 해석할 수 있습니다.
✅ 2. 변동성 돌파 전략에 활용
C-ATR+Vol이 80 이상인 구간에서 가격이 특정 저항선을 돌파한다면, 강한 추세의 시작을 의미할 수 있습니다.
반대로, C-ATR+Vol이 50 이하에서 가격이 저항선에 가까워지면 돌파 가능성이 낮아질 수 있습니다.
✅ 3. 시장 참여도와 변동성 확인
단순히 ATR만 높아서는 신뢰하기 어려운 경우가 많습니다. 예를 들어, 급등 후 거래량이 급감하면 상승 지속 가능성이 낮아질 수도 있습니다.
하지만 C-ATR+Vol을 사용하면 거래량이 함께 증가하는지를 확인하여 보다 신뢰할 수 있는 분석이 가능합니다.
🚀 결론
🔹 Combined ATR + Volume (C-ATR+Vol) 인디케이터는 단순한 ATR이 아니라 거래량까지 고려하여 변동성을 측정하는 강력한 도구입니다.
🔹 시장이 큰 움직임을 보일 가능성이 높은 구간을 찾는 데 유용하며, 80 이상일 경우 강한 변동성이 있음을 나타냅니다.
🔹 단독으로 사용하기보다는 보조지표와 함께 활용하여, 트렌드 분석 및 돌파 전략 등에 효과적으로 적용할 수 있습니다.
📌 주의사항
변동성이 크다고 해서 반드시 가격이 급등/급락한다는 보장은 없습니다.
특정한 매매 전략 없이 단순히 이 지표만 보고 매수/매도를 결정하는 것은 위험할 수 있습니다.
시장 상황에 따라 변동성의 의미가 다르게 작용할 수 있으므로, 반드시 다른 보조지표와 함께 활용하는 것이 중요합니다.
🔥 이 지표를 활용하여 시장의 변동성과 거래량을 보다 효과적으로 분석해보세요! 🚀
Volatility Momentum Breakout StrategyDescription:
Overview:
The Volatility Momentum Breakout Strategy is designed to capture significant price moves by combining a volatility breakout approach with trend and momentum filters. This strategy dynamically calculates breakout levels based on market volatility and uses these levels along with trend and momentum conditions to identify trade opportunities.
How It Works:
1. Volatility Breakout:
• Methodology:
The strategy computes the highest high and lowest low over a defined lookback period (excluding the current bar to avoid look-ahead bias). A multiple of the Average True Range (ATR) is then added to (or subtracted from) these levels to form dynamic breakout thresholds.
• Purpose:
This method helps capture significant price movements (breakouts) while ensuring that only past data is used, thereby maintaining realistic signal generation.
2. Trend Filtering:
• Methodology:
A short-term Exponential Moving Average (EMA) is applied to determine the prevailing trend.
• Purpose:
Long trades are considered only when the current price is above the EMA, indicating an uptrend, while short trades are taken only when the price is below the EMA, indicating a downtrend.
3. Momentum Confirmation:
• Methodology:
The Relative Strength Index (RSI) is used to gauge market momentum.
• Purpose:
For long entries, the RSI must be above a mid-level (e.g., above 50) to confirm upward momentum, and for short entries, it must be below a similar threshold. This helps filter out signals during overextended conditions.
Entry Conditions:
• Long Entry:
A long position is triggered when the current closing price exceeds the calculated long breakout level, the price is above the short-term EMA, and the RSI confirms momentum (e.g., above 50).
• Short Entry:
A short position is triggered when the closing price falls below the calculated short breakout level, the price is below the EMA, and the RSI confirms momentum (e.g., below 50).
Risk Management:
• Position Sizing:
Trades are sized to risk a fixed percentage of account equity (set here to 5% per trade in the code, with each trade’s stop loss defined so that risk is limited to approximately 2% of the entry price).
• Stop Loss & Take Profit:
A stop loss is placed a fixed ATR multiple away from the entry price, and a take profit target is set to achieve a 1:2 risk-reward ratio.
• Realistic Backtesting:
The strategy is backtested using an initial capital of $10,000, with a commission of 0.1% per trade and slippage of 1 tick per bar—parameters chosen to reflect conditions faced by the average trader.
Important Disclaimers:
• No Look-Ahead Bias:
All breakout levels are calculated using only past data (excluding the current bar) to ensure that the strategy does not “peek” into future data.
• Educational Purpose:
This strategy is experimental and provided solely for educational purposes. Past performance is not indicative of future results.
• User Responsibility:
Traders should thoroughly backtest and paper trade the strategy under various market conditions and adjust parameters to fit their own risk tolerance and trading style before live deployment.
Conclusion:
By integrating volatility-based breakout signals with trend and momentum filters, the Volatility Momentum Breakout Strategy offers a unique method to capture significant price moves in a disciplined manner. This publication provides a transparent explanation of the strategy’s components and realistic backtesting parameters, making it a useful tool for educational purposes and further customization by the TradingView community.
India VIXThe VIX chart represents the Volatility Index, commonly referred to as the "Fear Gauge" of the stock market. It measures the market's expectations of future volatility over the next 30 days, based on the implied volatility of NSE index options. The VIX is often used as an indicator of investor sentiment, reflecting the level of fear or uncertainty in the market.
Here’s a breakdown of what you might observe on a typical VIX chart:
VIX Value: The y-axis typically represents the VIX index value, with higher values indicating higher levels of expected market volatility (more fear or uncertainty), and lower values signaling calm or stable market conditions.
VIX Spikes: Large spikes in the VIX often correspond to market downturns or periods of heightened uncertainty, such as during financial crises or major geopolitical events. A high VIX is often associated with a drop in the stock market.
VIX Drops: A decline in the VIX indicates a reduction in expected market volatility, usually linked with periods of market calm or rising stock prices.
Trend Analysis: Technical traders might use moving averages or other indicators on the VIX chart to assess the potential for future market movements.
Inverse Relationship with the Stock Market: Typically, there is an inverse correlation between the VIX and the stock market. When stocks fall sharply, volatility increases, and the VIX tends to rise. Conversely, when the stock market rallies or remains stable, the VIX tends to fall.
A typical interpretation would be that when the VIX is low, the market is relatively stable, and when the VIX is high, the market is perceived to be uncertain or volatile.
Larry Conners Vix Reversal II Strategy (approx.)This Pine Script™ strategy is a modified version of the original Larry Connors VIX Reversal II Strategy, designed for short-term trading in market indices like the S&P 500. The strategy utilizes the Relative Strength Index (RSI) of the VIX (Volatility Index) to identify potential overbought or oversold market conditions. The logic is based on the assumption that extreme levels of market volatility often precede reversals in price.
How the Strategy Works
The strategy calculates the RSI of the VIX using a 25-period lookback window. The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is often used to identify overbought and oversold conditions in assets.
Overbought Signal: When the RSI of the VIX rises above 61, it signals a potential overbought condition in the market. The strategy looks for a RSI downtick (i.e., when RSI starts to fall after reaching this level) as a trigger to enter a long position.
Oversold Signal: Conversely, when the RSI of the VIX drops below 42, the market is considered oversold. A RSI uptick (i.e., when RSI starts to rise after hitting this level) serves as a signal to enter a short position.
The strategy holds the position for a minimum of 7 days and a maximum of 12 days, after which it exits automatically.
Larry Connors: Background
Larry Connors is a prominent figure in quantitative trading, specializing in short-term market strategies. He is the co-author of several influential books on trading, such as Street Smarts (1995), co-written with Linda Raschke, and How Markets Really Work. Connors' work focuses on developing rules-based systems using volatility indicators like the VIX and oscillators such as RSI to exploit mean-reversion patterns in financial markets.
Risks of the Strategy
While the Larry Connors VIX Reversal II Strategy can capture reversals in volatile market environments, it also carries significant risks:
Over-Optimization: This modified version adjusts RSI levels and holding periods to fit recent market data. If market conditions change, the strategy might no longer be effective, leading to false signals.
Drawdowns in Trending Markets: This is a mean-reversion strategy, designed to profit when markets return to a previous mean. However, in strongly trending markets, especially during extended bull or bear phases, the strategy might generate losses due to early entries or exits.
Volatility Risk: Since this strategy is linked to the VIX, an instrument that reflects market volatility, large spikes in volatility can lead to unexpected, fast-moving market conditions, potentially leading to larger-than-expected losses.
Scientific Literature and Supporting Research
The use of RSI and VIX in trading strategies has been widely discussed in academic research. RSI is one of the most studied momentum oscillators, and numerous studies show that it can capture mean-reversion effects in various markets, including equities and derivatives.
Wong et al. (2003) investigated the effectiveness of technical trading rules such as RSI, finding that it has predictive power in certain market conditions, particularly in mean-reverting markets .
The VIX, often referred to as the “fear index,” reflects market expectations of volatility and has been a focal point in research exploring volatility-based strategies. Whaley (2000) extensively reviewed the predictive power of VIX, noting that extreme VIX readings often correlate with turning points in the stock market .
Modified Version of Original Strategy
This script is a modified version of Larry Connors' original VIX Reversal II strategy. The key differences include:
Adjusted RSI period to 25 (instead of 2 or 4 commonly used in Connors’ other work).
Overbought and oversold levels modified to 61 and 42, respectively.
Specific holding period (7 to 12 days) is predefined to reduce holding risk.
These modifications aim to adapt the strategy to different market environments, potentially enhancing performance under specific volatility conditions. However, as with any system, constant evaluation and testing in live markets are crucial.
References
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Volatility System by W. WilderVolatility System (Volatility Stops) Similarity
Most traders adjust their stops over time in the direction of the trend in order to lock in profits. Apart from moving averages, one of the most popular techniques is trailing stops using a multiple of Average True Range. There are several variations:
The original Volatility System(Volatility Stops), introduced by Welles Wilder in his 1978 book: New Concepts in Technical Trading Systems
Chandelier exits introduced by Alexander Elder in Come Into My Trading Room (2002) trail the stops from Highs or Lows rather than Closing Price
Average True Range Trailing Stops are similar to the above, but include a ratchet mechanism to prevent stops moving down during an up-trend or rising during a down-trend, as ATR increases
WillTrend intoduced by Larry Williams in 1988
Comparison of systems
All the systems under consideration have one common ingredient - ATR. ATR was developed by Welles Wilder and described in his book in 1978, also in this book the Volatility System was described, which in the future became known as Volatility Stops.
In fact, Wilder is the father of such systems due to the presence of ATR in the calculation of this type of indicator.
The main difference of Volatility System
Followers such as Larry Williams and Alexander Elder made minor changes to the value based on the ATR, mainly focusing on changing the base to which this value is added or subtracted.
Larry Williams uses the square root of 5 as a multiplier and calculates the ATR with a period of 66, and Alexander Elder uses a multiplier of 2.5-3.5 applying it to the ATR with a period of 22. Both authors changed the original value for ATR and multiplier calculations. Alexander Elder is closest to the original Welles Wilder calculation, which used a multiplier of 2.8.-3.1 applying it to an ATR with a period of 7.
As a reference, Elder took the Highest High(22) from which he subtracts ATR*Multiplier in an uptrend or the Lowest Low(22) to which he adds ATR*Multiplier to obtain the turning point (SAR).
Larry Williams uses the average price of extremes (Highest High(10) + Lowest Low(10)) / 2 as a reference base to which he adds or subtracts the ATR*Multilpyer values.
Both systems differ from the original, because Wilder used Significan Close(SIC) in his calculations. SIC is the maximum closing price during an uptrend and the minimum closing price during a downtrend, which
does not go beyond the current trade, as in other systems. To calculate the base when a trend changes, bars that are outside the current trend will be used when calculating WillTrend and Chandelier Exit, in contrast to the Volatility System, which takes SIC values only within the current trade. This is the main difference from subsequent developments of similar systems.
Improvements made
The original Volatility System is present as an indicator on TradingView, but it is an improved version with the addition of a ratchet and works differently from the original Weilder system.
List of improvements:
Added the ability to remove the ratchet. You need to turn off the "Trail one way" checkbox in the setting menu. When this function is turned off, the system will operate in the author-inventor mode. On some instruments, the original system works much better than the improved ratchet system, which cannot be turned off.
Added the ability to use Highest High and Lowest Low as a base instead of the closing price.
Volatility Stops Formula Description
Welles Wilder's system uses Closing Price and incorporates a stop-and-reverse feature (as with his Parabolic SAR).
Determine the initial trend direction
Calculate the Significant Close ("SIC"): the highest close reached in an up-trend or the lowest close in a down-trend
Calculate Average True Range ("ATR") for the selected period (7 days in this example)
Multiply ATR by the Multiple (3.0 in this example, best values author describes as 2.8-3.1)
The first stop is calculated in day 7 and plotted for day 8
If an up-trend, the first stop is SIC - 3 * ATR, otherwise SIC + 3 * ATR for a down-trend
Repeat each day until price closes below the stop (or above in a down-trend)
Set SIC equal to the latest Close, reverse the trend and continue.
Chandelier Exit Description
Chandelier Exits subtract a multiple of Average True Range ("ATR") from the highest high for the selected period. Using the default settings as an example:
Highest High in last 22 days - 3 * ATR for 22 days
In a down-trend the formula is reversed:
Lowest Low in last 22 days + 3 * ATR for 22 days
The time period must be long enough to capture the highest point of the recent up-trend: too short and the stops move downward; too long and the high may be taken from a previous down-trend.
It is not essential to use the same period for up and down trends; down-trends are notoriously faster than up-trends and may benefit from a shorter time period.
The multiple of 3 may be varied, but most traders settle between 2.5 and 3.5.
WillTrend Description
Larry Williams is prefer to used the Square Root from 5 as a multiplayer for ATR. SQRT(5) = 2.236
WillTrend subtract a multiple of Average True Range ("ATR") from the Middle Price (Highest High for the selected period + Lowest Low for the selected period / 2).
(Highest High in last 10 days + Lowest Low in last 10 days) / 2 - 2.236 * ATR for 66 days
In a down-trend the formula is reversed:
(Highest High in last 10 days + Lowest Low in last 10 days) / 2 + 2.236 * ATR for 66 days
Volatility Price RangeThe Volatility Price Range is an overlay which estimates a price range for the next seven days and next day, based on historical volatility (already available in TradingView). The upper and lower bands are calculated as follows:
The Volatility for one week is calculated using the formula: WV = HV * √t where:
WV: one-week volatility
HV: annual volatility
√: square root
t: the time factor expressed in years
From this formula we can deduce the weekly volatility WV = HV * √(1 / 52) = HV / 7.2 where 52: weeks in a year.
The daily volatility DV = HV * √(1 / 365) = HV / 19.1 where 365: days in a year.
To calculate the lower and upper value of the bands, the weekly/daily volatility value obtained will be subtracted/added from/to the current price.
Volatility Gap TrackerThe Volatility Gap Tracker ( *VGT ) indicator calculates the historical volatility of an asset using the standard deviation of the natural logarithm of the closing price relative to the previous period's closing price. *VGT visualizes the HV with gap lines to highlight when the current HV has increased or decreased significantly compared to the previous period, and adds labels to show the HV value for each of those bars.
Low HV calculated by *VGT can potentially signify a potential move up or down in the price of an asset. When HV is low, it indicates that the price of the asset has been relatively stable or range-bound over the specified period of time. This can sometimes be a precursor to a significant move in either direction, as the price may be building up energy to break out of its range.
*VGT can be used for any market that TradingView supports, including stocks, forex, and cryptocurrencies. It is especially useful for traders who want to identify periods of high volatility or sudden changes in volatility , which can indicate potential trading opportunities or risks. However, it's important to note that HV is a historical measure and may not always accurately predict future volatility .
The indicator can be used under various market conditions, but is especially useful during periods of high volatility , such as market crashes or major news events. It can also be useful for traders who want to monitor the volatility of specific stocks or assets over a longer period of time.
*VGT is provided for informational purposes only and is not a guarantee of future performance or accuracy. Traders should use multiple indicators and analysis methods to make informed trading decisions. Trading involves risks and traders should always conduct their own research and analysis before making any investment decisions.
Chervolinos-Wave-PM-ForecastThe Wave PM (Whistler Active Volatility Energy – Price Mass) indicator is an oscillator described in Mark Whistler's book, Volatility Illuminated.
The Wave PM is specifically designed to help read volatility cycles. When we visualize volatility cycles as a chart, we can get a clear view of the market volatility phases in multiple time frames. This indicator forms an arithmetic mean over 30 observed periods. Traders can thus get a better insight into "potential" volatility from up to pent-up energy, the different zones give strong help to predict future price developments.
Possible interpretation patterns:
You are at the end of a long uptrend and you want to know if the price is going to go down, if the indicator shows red and the value is above 25, it is likely to do so.
You're in a downtrend and there's a bit of a recovery phase, so you might be wondering if it's going to continue when the indicator shows green. It would go further with yellow, but with green it can be assumed that it is going down rapidly.
Special thanks to sourcey who programmed the 3D Wave-PM.
This variant of sourcey looks very nice, but was too confusing for me. In order to get a strong overview, forming an arithmetic mean is very useful.
I hope you and the Mods like my version
Best regards, Chervolino
Volume Adaptive Chikou Scalping StudyIDEA PLACEMENT
This indicator uses “Chikou” cross concept of Ichimoku cloud indicator and enhances usage of High/Low data with Volume Breakout and Volatility based dynamic adaption.
I’ve been working on making Moving Averages more adaptive based on Volume Breakout and Volatility but as we know Mas work better on close values. I wanted to create a study that may have maximum data available and that’s how I came up with the concept of making adaptive Ichimoku Cloud. Except, I used different concept than Ichimoku. As we know that Tenkan-sen and Kijun-sen from Ichimoku Cloud average out highest and lowest values within 26 and 9 period respectively but I tried making it Volume Breakout and Volatility based Adaptive but couldn’t get better results.
Along the way I came up with an idea of instead of averaging out just keeping the High/Low values data separate and intact and to do so I took Linear regression of High values of Volume Breakout and Volatility based Adaptive dynamic period and similarly with Low values.
Then the strategy was to use Chikou for crossover and crossunder indication and for this purpose I used Chikou with same dynamic length as used before in High/Low linear regression.
The idea becomes simple as when Adaptive Dynamic Chikou crosses Adaptive Dynamic Linear Regression of High/Low values then Lowest / Highest value within current Adaptive Dynamic Length becomes the next Support / Resistance.
SIGNALS
Not every Chikou cross would give signal instead signal should be supported by either Volume Breakout or Volatility whatever you have selected from.
FIBONACCI EVELOPE BANDS
I’ve included ATR based Fibonacci multiple bands which would act as good support/resistance zones.
DEFAULT SETTINGS
I’ve set default Minimum length to 20 and Maximum length to 50 which I’ve found works best for almost all timeframes but you can change this delta to adpat your timeframe accordingly with more precision.
Dynamic length adoption is enabled based on both Volume and Volatility but only one or none of them can also be selected.
Trend signals verification is enabled based on Volume but Volatility can also be enabled for more precise confirmations.
In “RVSI” settings TFS Volume Oscillator is set to default but others work good too especially Volume Zone Oscillator. For more details about Volume Breakout you can check “MZ RVSI Indicator”
ATR breakout is set to be true if period 14 exceeds period 46 but can be changed if more adaption with volatility is required.
FURTHER ENHANCEMENTS
I’ve used Linear Regression of High/Low values because I found better results with it but SMA and HMA can also be used. I’m planning to perpetually use this study for Dynamically length adaption and trades confirmations in other strategies.
Bollinger Bands With User Selectable MABollinger Bands with user selection options to calculate the moving average basis and bands from a variety of different moving averages.
The user selects their choice of moving average, and the bands automatically adjust. The user may select a MA that reacts faster to volatility or slower/smoother.
Added additional options to color the bands or basis based on the current trend and alternate candle colors for band touches. Options:
REACT SLOW/SMOOTH TO VOLATILITY
simple moving average (Regular Bollinger Bands)
REACT SMOOTH TO VOLATILITY
exponential moving average (EMA Bollinger Bands)
weighted moving average (Weighted MA Bollinger Bands)
exponential hull moving average (Hull Bollinger Bands with better smoothing)
HIGHLY ADJUSTABLE TO VOLATILITY
Arnaud Legoux Moving average (ALMA Bollinger Bands)
Note: 0.85 ALMA default for more smoothing, set offset=1 to turn off smoothing
REACT HARSH TO VOLATILITY
least squares moving average (Least Squares Bollinger Bands)
REACT VERY FAST TO VOLATILITY
hull moving average (Hull Bollinger Bands or Hullinger Bands)
VALUE ADDED: This script is unique in that no other Bollinger Bands indicator offers a user selection for moving average, and some of the options do not exist yet as Bollinger Bands indicators.
Definitions:
Bollinger Bands: A Bollinger Band® is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average (SMA) of a security's price, but which can be adjusted to user preferences.
Exponential Bollinger Bands: The most important characteristics of the Exponential Bollinger Bands indicator are: When the market is flat, the bands will stay much closer to prices. When the volatility is high, the bands move away from prices faster.
Hull Bollinger Bands: Bollinger Bands calculated by Hull moving average, rather than simple moving average or ema. The Hull Moving Average (HMA), developed by Alan Hull, is an extremely fast and smooth moving average. In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time.
Exponential Hull Bollinger Bands: Bollinger Bands calculated by Exponential Hull moving average, rather than simple moving average or ema. The Exponential Hull Moving Average is similar to the standard Hull MA, but with superior smoothing. The standard Hull Moving Average is derived from the weighted moving average (WMA). As other moving average built from weighted moving averages it has a tendency to exaggerate price movement.
Weighted Moving Average Bollinger Bands: A Weighted Moving Average (WMA) is similar to the simple moving average (SMA), except the WMA adds significance to more recent data points.
Arnaud Legoux Moving Average Bollinger Bands: ALMA removes small price fluctuations and enhances the trend by applying a moving average twice, once from left to right, and once from right to left. At the end of this process the phase shift (price lag) commonly associated with moving averages is significantly reduced. Zero-phase digital filtering reduces noise in the signal. Conventional filtering reduces noise in the signal, but adds a delay.
Least Squares Bollinger Bands: The indicator is based on sum of least squares method to find a straight line that best fits data for the selected period. The end point of the line is plotted and the process is repeated on each succeeding period.
vol_signalNote: This description is copied from the script comments. Please refer to the comments and release notes for updated information, as I am unable to edit and update this description.
----------
USAGE
This script gives signals based on a volatility forecast, e.g. for a stop
loss. It is a simplified version of my other script "trend_vol_forecast", which incorporates a trend following system and measures performance. The "X" labels indicate when the price touches (exceeds) a forecast. The signal occurs when price crosses "fcst_up" or "fcst_down".
There are only three parameters:
- volatility window: this is the number of periods (bars) used in the
historical volatility calculation. smaller number = reacts more
quickly to changes, but is a "noisier" signal.
- forecast periods: the number of periods for projecting a volatility
forecast. for example, "21" on a daily chart means the plots will
show the forecast from 21 days ago.
- forecast stdev: the number of standard deviations in the forecast.
for example, "2" means that price is expected to remain within
the forecast plot ~95% of the time. A higher number produces a
wider forecast.
The output table shows:
- realized vol: the volatility over the previous N periods, where N =
"volatility window".
- forecast vol: the realized volatility from N periods ago, where N =
"forecast periods"
- up/down fcst (level): the price level of the forecast for the next
N bars, where N = "forecast periods".
- up/down fcst (%): the difference between the current and forecast
price, expressed as a whole number percentage.
The plots show:
- blue/red plot: the upper/lower forecast from "forecast periods" ago.
- blue/red line: the upper/lower forecast for the next
"forecast periods".
- red/blue labels: an "X" where the price touched the forecast from
"forecast periods" ago.
+ NOTE: pinescript only draws a limited number of labels.
They will not appear very far into the past.
Williams Vix Fix + BB & RVI (Top/Bottom) & SqueezeLegend :
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Squeeze signal
This is an attempt to make use of the main features of all 4 of these very popular Volatility tools :
- Williams Vix Fix + Bollinger Bands (as per Larry Williams idea, link )
- Relative Volatility Index (RVI)
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
The goal is to find the best tool to find bottoms and top relative to volatility . This is a simple combination, but I find it very useful personally
(no need to reinvent the wheel, just need to find what works best)
The idea is that Williams Vix Fix + Bollinger Bands already give the main volatility bottom and top (Bottom are more accurate).
So instead of trying to modify it, I chose to compliment it by mapping with points when the Relative Volatility Index (RVI) reached the
top/bottom thresholds (red dot means top and blue dot means bottom). That way we can easily see when both indicators find a top or bottom relative
to volatility (of course this needs to be then confirmed with a momentum indicator rally).
In addition, I added the squeeze because this quickly shows the potential breakouts.
For ideas on how to continue this work, it would be very interesting to be able to create a probability of a bottom and top relative to volatility using the
Williams Vix Fix + Bollinger Bands and "Relative Volatility Index" signals as both work well and give top or bottom the other doesn't see.
BBW, Squeeze, and EMAThis indicator helps traders to identify low volatility areas by using the "Bollinger Bands Squeeze" method. The idea is to look for the lowest values in the Bollinger bands width to judge the lowest volatility readings.
By default the squeeze period is 125, as suggested by John Bollinger.
The indicator is a variation on the BB Squeeze indicator by @danarm. It adds:
* A lowest volatility print, named "Squeeze Line". This shows traders the lowest BBW reads.
* A moving average on top of BBW. This helps traders gauge volatility expansion. I'd use is like this: once you identify volatility contraction, an expansion would be when BBW crosses its average upwards.
* The blue background color indicates where BBW crossed its lowest 125 value, indicating further volatility contraction.
Dual Mean Reversion Channel (adjusted lower band)This is a public and open-source lighter version compared to the "Overextended Price Channel" which is provided complimentaty to the Trend Insight System.
Introduction :
Channels are very useful tools to assess overextended price, volatility and upcoming retracement or impulsive moves (such as Bollinger Band squeezes). It is an indispensable addition to any trader using Mean Reversion theory for a scalp-trade or swing-trade.
This script contains :
- 2 channels Keltner-style, using the True Range for volatility
- customizable volatility (channel width) and smoothing period
- a standard selection of moving average ; SMA, EMA, VWMA
- an embedded readjustment of the lower bands to avoid the drop on a logarithmic scale (see explanation below)
Why another channel indicator ?
I have found most conventional channels to be either not based on "proper" volatility (e.g. standard deviation of price action for Bollinger Band), or the bottom channel to be ill adapted to the logarithmic scale and plunges to 0 on some high volatility periods, messing with readability on logarithmic auto-scaled chart.
Also, I find the channels to be most useful when superimposed with another one of longer length; especially a pair of channels with a 50 and 200 period moving average respectively. Mean Reversion traders that mostly trade the 50 and 200 SMA/EMA know what I am talking about as having a channel helps to have a better visual for a proper of entry and exit point.
Disclaimer :
This indicator was originally intended to be used along with the Trend Insight System to improve performance, and the default configuration mostly backtested on BTCUSD.
Please use with caution, proper risk management and along with your favorite oscillator, candlestick reading and signals system.
Some explanation :
Based on Mean Reversion paradigm, everything has a tendency to revert back to the mean :
- when the price enters the upper channel, it is supposed to be (or start getting) overbought as the market is getting overheated, thus prone to correction,
- on the other hand, when the price enters the lower channel, it is supposed to be (or getting) oversold and the market looks favorable for a buy-in.
Depending on the trading style used, a trader will usually either wait until the price leaves the channel towards the mean before taking action (conservative style) or you will set limit orders inside the channel as you expect a reversion to the mean (more agressive/risky style).
With two channels, more complex (and maybe precise) rules can be built to optimize one's trading strategy.
Important notes :
In the end, sticking with 50/200 length and a single setting on volatility might be wiser, be wary of overoptimization which is risky at best and counter productive at worst (according to legendary traders such as Mark Douglas). Even if, needless to say, the volatility needs to be adjusted between a nascent and volatile market (such as crypto) compared to standard call markets that are much less volatile.
End notes :
It will always be considered a work in progress to help bring out the best of trading with channels, any comment and suggestion are welcomed.
Volatility Prism [Nic]What is this
The volatility rainbow tracks divergences in a security and its volatility index. This can be used to identify periods of heightened implied (future) risk.
About Volatility
The volatility is calculated by looking at put / call ratios. When VIX goes up it means that puts are outpacing calls. This is a bearish signal.
About Correlation
When the security goes up while the VIX goes up, the divergence on the plot will increase and turn a color. This should be a warning.
Volatility Rainbow
This is a similar indicator, but this one merges all signals into a single line.
