Dynamically Adjustable Moving AverageIntroduction
The Dynamically Adjustable Moving Average (AMA) is an adaptive moving average proposed by Jacinta Chan Phooi M’ng (1) originally provided to forecast Asian Tiger's futures markets. AMA adjust to market condition in order to avoid whipsaw trades as well as entering the trending market earlier. This moving average showed better results than classical methods (SMA20, EMA20, MAC, MACD, KAMA, OptSMA) using a classical crossover/under strategy in Asian Tiger's futures from 2014 to 2015.
Dynamically Adjustable Moving Average
AMA adjust to market condition using a non-exponential method, which in itself is not common, AMA is described as follow :
1/v * sum(close,v)
where v = σ/√σ
σ is the price standard deviation.
v is defined as the Efficacy Ratio (not be confounded with the Efficiency Ratio) . As you can see v determine the moving average period, you could resume the formula in pine with sma(close,v) but in pine its not possible to use the function sma with variables for length, however you can derive sma using cumulation.
sma ≈ d/length where d = c - c_length and c = cum(close)
So a moving average can be expressed as the difference of the cumulated price by the cumulated price length period back, this difference is then divided by length. The length period of the indicator should be short since rounded version of v tend to become less variables thus providing less adaptive results.
AMA in Forex Market
In 2014/2015 Major Forex currencies where more persistent than Asian Tiger's Futures (2) , also most traded currency pairs tend to have a strong long-term positive autocorrelation so AMA could have in theory provided good results if we only focus on the long term dependency. AMA has been tested with ASEAN-5 Currencies (3) and still showed good results, however forex is still a tricky market, also there is zero proof that switching to a long term moving average during ranging market avoid whipsaw trades (if you have a paper who prove it please pm me) .
Conclusion
An interesting indicator, however the idea behind it is far from being optimal, so far most adaptive methods tend to focus more in adapting themselves to market complexity than volatility. An interesting approach would have been to determine the validity of a signal by checking the efficacy ratio at time t . Backtesting could be a good way to see if the indicator is still performing well.
References
(1) J.C.P. M’ng, Dynamically adjustable moving average (AMA’) technical
analysis indicator to forecast Asian Tigers’ futures markets, Physica A (2018),
doi.org
(2) www.researchgate.net
(3) www.ncbi.nlm.nih.gov
在腳本中搜尋"Volatility"
DD ATR bandsThe top band is ATR added to candle high (with given length and multiplication). The bottom one is analogic.
Created for finding initial stop loss for entry on low timeframes. Use band value at last major high/low to place the stop loss at.
It shows prices with acceptable risk and a reasonable margin for market volatility.
Brooks Type Signal BarIndicates "strong bars" similar to how Al Brooks defines them in his book-- these don't necessarily trigger entries but can be points of interest.
2-3 points as a signal bar size seems to work well, depending upon volatility.
btcATR Bitfinex [csg]A simple script that uses the average true range indicator and compares it against bitcoin's market cap to obtain a visual representation of bitcoin's historical volatility.
A version of this indicator will be made for other coins once tradingview gives us access to the market cap data
Bollinger Band Percent Width Crossing RSIBollinger Band width is hard to follow on log charts, but it's an excellent indicator for volatility. When BBand % width crosses down through periods of low crosses down through periods where RSI is low, you may be able to count on a substantial reversal over the intermediate or long-term.
Directional Movement Bands [DW]This is a simple experimental study designed to outline trend activity and volatility.
In this study, the amount of change between current source and source of a specified lookback is calculated, then added to and subtracted from current source.
Next an exponential moving average is taken of the values for smoothing over the specified period.
Lastly, a midline is generated by taking the median of both bands.
Volatility Finite Volume Elements Strategy The FVE is a pure volume indicator. Unlike most of the other indicators
(except OBV), price change doesn?t come into the equation for the FVE
(price is not multiplied by volume), but is only used to determine whether
money is flowing in or out of the stock. This is contrary to the current trend
in the design of modern money flow indicators. The author decided against a
price-volume indicator for the following reasons:
- A pure volume indicator has more power to contradict.
- The number of buyers or sellers (which is assessed by volume) will be the same,
regardless of the price fluctuation.
- Price-volume indicators tend to spike excessively at breakouts or breakdowns.
This study is an addition to FVE indicator. Indicator plots different-coloured volume
bars depending on volatility.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not
Volatility Finite Volume Elements Strategy The FVE is a pure volume indicator. Unlike most of the other indicators
(except OBV), price change doesn?t come into the equation for the FVE
(price is not multiplied by volume), but is only used to determine whether
money is flowing in or out of the stock. This is contrary to the current trend
in the design of modern money flow indicators. The author decided against a
price-volume indicator for the following reasons:
- A pure volume indicator has more power to contradict.
- The number of buyers or sellers (which is assessed by volume) will be the same,
regardless of the price fluctuation.
- Price-volume indicators tend to spike excessively at breakouts or breakdowns.
This study is an addition to FVE indicator. Indicator plots different-coloured volume
bars depending on volatility.
Moving CO-covariance (covariance on covariance)This is Covariance on Covariance. It shows you how much a given covariance period has deviated from it mean over another defined period. Because it is a time series, It can allow you to spot changes in how covariance changes. You can apply trend lines, Fibonacci retracements, etc. This is also volume weighting covariance.
This is not a directional indicator nor is moving covariance. This is used for forecasting volatility. This must be used in conjunction with moving covariance.
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
[Volatility] [Gain & Loss] - OverviewFX:EURUSD
Indicator Overview: Volatility & Gain/Loss - Forex Pair Analysis
This indicator, " —Overview" , is designed for users interested in analyzing the volatility and gain/loss metrics of multiple forex pairs. The tool is especially useful for traders aiming to assess currency pair volatility alongside gain and loss percentages over selected periods. It enables a clearer understanding of pair behavior and aids in decision-making.
Key Features
Customizable Volatility and Gain/Loss Periods : Define your preferred calculation periods and timeframes for both volatility and gain/loss to tailor the indicator to specific trading strategies. Multi-Pair Analysis : This indicator supports up to six forex pairs (default pairs include EURUSD, GBPUSD, USDJPY, USDCHF, AUDUSD, and USDCAD) and allows you to adjust these pairs as needed. Visual Ranking : Forex pairs are sorted by volatility, displaying the highest pairs at the top for quick reference. Top Gain/Loss Highlighting : The pair with the maximum gain and the pair with the maximum loss are highlighted in the table, making it easy to identify the best and worst performers at a glance.
Indicator Settings
Volatility Settings : Period : Adjust the number of periods used in the ATR (Average True Range) calculation. A default period of 14 is set. Timeframe : Select a timeframe (e.g., Daily, Weekly) for volatility calculation to match your analysis preference.
Gain/Loss Settings : Period : Choose the number of periods for gain/loss calculation. The default is set to 1. Timeframe : Select the timeframe for gain/loss calculation, independent of the volatility timeframe.
Symbol Selection : Configure up to six forex pairs. By default, popular forex pairs are pre-loaded but can be customized to include other currency pairs.
Output and Visualization
Table Display : This indicator displays data in a neatly structured table positioned in the top-right corner of your chart. Columns : Includes columns for the Forex Pair, Volatility Percentage, Gain Percentage, and Loss Percentage. Color Coding : Volatility is displayed in a standard color for clear readability. Gain values are highlighted in green, and Loss values are highlighted in red, allowing for quick visual differentiation. Highlighting : Rows representing the pair with the highest gain and the pair with the most significant loss are especially highlighted for emphasis.
How to Use
Volatility Analysis : This metric gives insight into the average price range movements for each pair over the specified period and timeframe, helping you evaluate the potential for rapid price changes. Gain/Loss Tracking : Gain or loss percentages show the pair's recent performance, allowing you to observe whether a currency pair is trending positively or negatively over the chosen period. Comparative Pair Ranking : Use the table to identify pairs with the highest volatility and extremes in gain or loss to guide trading decisions based on market conditions.
Ideal For
Swing Traders and Day Traders looking to understand short-term market fluctuations in currency pairs. Risk Management : Helps traders gauge pairs with higher risk (volatility) and recent performance (gain/loss) for informed position sizing and risk control.
This indicator is a comprehensive tool for visualizing and analyzing key forex pairs, making it an essential addition for traders looking to stay updated on volatility trends and recent price changes.
Standard Deviation OscillatorStandard Deviation Oscillator (STDEV OSC) v1.1
Description
The Standard Deviation Oscillator transforms traditional volatility measurements into a dynamic oscillator that fluctuates between 0 and 100. This advanced technical analysis tool helps traders identify periods of extreme volatility and potential market turning points.
Features
Normalized volatility readings (0-100 scale)
Dynamic color changes based on volatility levels
Customizable overbought/oversold thresholds
Built-in alert conditions
Adaptive calculation using rolling windows
Clean, professional visualization
Indicator Parameters
Length: 20; Calculation period for standard deviation
Source: close; Price source for calculations
Overbought Level: 70; Upper threshold for high volatility
Oversold Level: 30; Lower threshold for low volatility
Visual Components
- Main Oscillator Line: Changes color based on current level
- Red: Above overbought level
- Green: Below oversold level
- Blue: Normal range
- Reference Lines:
- Overbought level (default: 70)
- Oversold level (default: 30)
- Middle line (50)
Alert Conditions
1. Volatility High Alert
- Triggers when oscillator crosses above the overbought level
- Useful for identifying potential market tops or breakout scenarios
2. Volatility Low Alert
- Triggers when oscillator crosses below the oversold level
- Helps identify potential market bottoms or consolidation periods
Risk Adjustment Tool
- Scale position sizes inversely to oscillator readings
- Reduce exposure during extremely high volatility periods
- Increase position sizes during normal volatility conditions
Best Practices
1. Timeframe Selection
- Best suited for 1H, 4H, and Daily charts
- Adjust length parameter based on timeframe
2. Confirmation
- Use in conjunction with trend indicators
- Confirm signals with price action patterns
- Consider overall market context
3. Parameter Optimization
- Backtest different length settings
- Adjust overbought/oversold levels based on asset
- Consider market conditions when setting alerts
Technical Notes
- Built in PineScript v5
- Optimized for TradingView platform
- Uses rolling window calculations for better adaptability
- Compatible with all trading instruments
- Minimal performance impact on charts
Version History
- v1.1: Added dynamic coloring, customizable levels, and alert conditions
- v1.0: Initial release with basic oscillator functionality
Disclaimer
This technical indicator is provided for educational and informational purposes only. Past performance is not indicative of future results. Always conduct thorough testing and use proper risk management techniques.
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Tags: #TechnicalAnalysis #Volatility #Trading #Oscillator #TradingView #PineScript
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
VIX HeatmapVIX HeatMap
Instructions:
- To be used with the S&P500 index (ES, SPX, SPY, any S&P ETF) as that's the input from where the CBOE calculates and measures the VIX. Can also be used with the Dow Jones, Nasdaq, & Nasdaq100.
Description:
- Expected Implied Volatility regime simplified & visualized. Know if we are in a high, medium, or low volatility regime, instantly.
- Ranges from Hot to Cold: The hotter the heat-map, the higher the implied volatility and fear & vice versa.
- The VIX HeatMap, color-maps important VIX levels (7 in this case) in measuring volatility for day trading & swing trading.
Using the VIX HeatMap:
- A LOW level volatility environment: Represented by "cooler" colors (Blue & White) depicts that the level of volatility and fear is low. Percentage moves on the index level are going to be tame and less volatile more often than not. Low fear = low perceived risk.
- A MEDIUM level volatility environment: Represented by "warmer" colors (Green & Yellow) depicts that the markets are transitioning from a calmer period or from a more fearful period. Market volatility here will be higher and provide more volatile swings in price.
- A HIGH level volatility environment: Represented by "hotter" colors (Orange, Red, & Purple) depicts that the markets are very fearful at the moment and will have big swings in both directions. Historically, extreme VIX levels tend to coincide with bottoms but are in no way predictive of the exact timing as the volatile moves can continue for an extended period of time.
- Transitioning between the 7 VIX Zones: Each and every one of these specific VIX zone levels is important.
1. Extreme low: <16
2. Low: 16 to 20
3. Normal: 20 to 24
4. Medium: 24 to 28
5. Med-High: 28 to 32
6. High: 32 to 36
7. Extreme high: >36
- These VIX levels in particular measure volatility changes that have a major impact on switching between smaller time frames and measuring depths of a sell move and vice versa. Each level also behaves as its own support & resistance level in terms of taking a bit of effort to switch regimes, and aids in identifying and measuring the potential depth of pullbacks in bull markets and bounces in bear markets to reveal reversal points.
- Examples of VIX level supports depicted on the chart marked with arrows. From left to right:
1. March 10th: Markets jumped 2 volatility levels in 2 days. The fluctuations from blue to yellow to green where a sign that price action would reverse from the selloff.
2. March 28th: As soon as we move from green to the blue VIX level (<20), markets began to rally and only ended when the volatility level moved sub VIX 16 (white).
3. May 4th & 24th: Next we see the 2 dips where volatility levels went from blue to green (VIX > 20), marked bottoms and reversed higher.
4. June 1st: We see a change in VIX regime yet again into lower VIX level and markets rocket higher.
Knowing the current VIX regime is a very important tool and aid in trading, now easily visualized.
Bar metrics / quantifytools— Overview
Rather than eyeball evaluating bullishness/bearishness in any given bar, bar metrics allow a quantified approach using three basic fundamental data points: relative close, relative volatility and relative volume. These data points are visualized in a discreet data dashboard form, next to all real-time bars. Each value also has a dot in front, representing color coded extremes in the values.
Relative close represents position of bar's close relative to high and low, high of bar being 100% and low of bar being 0%. Relative close indicates strength of bulls/bears in a given bar, the higher the better for bulls, the lower the better for bears. Relative volatility (bar range, high - low) and relative volume are presented in a form of a multiplier, relative to their respective moving averages (SMA 20). A value of 1x indicates volume/volatility being on par with moving average, 2x indicates volume/volatility being twice as much as moving average and so on. Relative volume and volatility can be used for measuring general market participant interest, the "weight of the bar" as it were.
— Features
Users can gauge past bar metrics using lookback via input menu. Past bars, especially recent ones, are helpful for giving context for current bar metrics. Lookback bars are highlighted on the chart using a yellow box and metrics presented on the data dashboard with lookback symbols:
To inspect bar metric data and its implications, users can highlight bars with specified bracket values for each metric:
When bar highlighter is toggled on and desired bar metric values set, alert for the specified combination can be toggled on via alert menu. Note that bar highlighter must be enabled in order for alerts to function.
— Visuals
Bar metric dots are gradient colored the following way:
Relative volatility & volume
0x -> 1x / Neutral (white) -> Light (yellow)
1x -> 1.7x / Light (yellow) -> Medium (orange)
1.7x -> 2.4x / Medium (orange) -> Heavy (red)
Relative close
0% -> 25% / Heavy bearish (red) -> Light bearish (dark red)
25% -> 45% / Light bearish (dark red) -> Neutral (white)
45% - 55% / Neutral (white)
55% -> 75% / Neutral (white) -> Light bullish (dark green)
75% -> 100% / Light bullish (dark green) -> Heavy bullish (green)
All colors can be adjusted via input menu. Label size, label distance from bar (offset) and text format (regular/stealth) can be adjusted via input menu as well:
— Practical guide
As interpretation of bar metrics is highly contextual, it is especially important to use other means in conjunction with the metrics. Levels, oscillators, moving averages, whatever you have found useful for your process. In short, relative close indicates directional bias and relative volume/volatility indicates "weight" of directional bias.
General interpretation
High relative close, low relative volume/volatility = mildly bullish, bias up/consolidation
High relative close, medium relative volume/volatility = bullish, bias up
High relative close, high relative volume/volatility = exuberantly bullish, bias up/down depending on context
Medium relative close, low relative volume/volatility = noise, no bias
Medium relative close, medium to high relative volume/volatility = indecision, further evidence needed to evaluate bias
Low relative close, low relative volume/volatility = mildly bearish, bias down/consolidation
Low relative close, medium relative volume/volatility = bearish, bias down
Low relative close, high relative volume/volatility = exuberantly bearish, bias down/up depending on context
Nuances & considerations
As to relative close, it's important to note that each bar is a trading range when viewed on a lower timeframe, ES 1W vs. ES 4H:
When relative close is high, bulls were able to push price to range high by the time of close. When relative close is low, bears were able to push price to range low by the time of close. In other words, bulls/bears were able to gain the upper hand over a given trading range, hinting strength for the side that made the final push. When relative close is around middle range (40-60%), it can be said neither side is clearly dominating the range, hinting neutral/indecision bias from a relative close perspective.
As to relative volume/volatility, low values (less than ~0.7x) imply bar has low market participant interest and therefore is likely insignificant, as it is "lacking weight". Values close to or above 1x imply meaningful market participant interest, whereas values well above 1x (greater than ~1.3x) imply exuberance. This exuberance can manifest as initiation (beginning of a trend) or as exhaustion (end of a trend):
Williams Vix Fix BB + RVI & Squeeze (Keltner) filtered BBW + %BLegend:
- 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 Keltner Squeeze signal (filtered by either BBW or %B)
- Silver dot at the bottom of indicator is a weaker Keltner Squeeze signal (Doesn't meet either BBW or %B filter)
- Purple is a 'Half Squeeze' only 1 Bollinger Band crossed the Keltner Channel
This is an attempt to make use of the main features of all 6 of these Volatility tools :
- Williams Vix Fix + Bollinger Bands
- Relative Volatility Index (RVI)
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
Conditions to Help Filter Keltner Squeeze:
- When the Bollinger Bands Width (BBW) value is lower than the lowest value within a period plus a margin of error (percentage)
- When the %B value reaches the alert level detailed in LazyBears indicator. ()
If it meets one of these 2 filters and there is a Keltner Channel Squeeze than gray color or else if the squeeze doesn’t meet one of the 2 filters than silver color (weaker Squeeze).
The goal is to find the best tool to find bottoms and top relative to volatility and filter the squeeze.
The idea is that both Williams Vix Fix + Bollinger Bands and Relative Volatility Index both already give the main volatility bottom and top so combining them to compare and validate the signals makes sense. (Note: Bottom signal is more accurate than top). In addition, I added the squeeze to show the potential breakout pressure and to compliment bottom and top signals.
For ideas on how to continue this work :
I encourage ideas to combine the Williams Vix Fix and Relative Volatility Index for volatility top and bottom (with probability would be awesome)
And I encourage ideas to filter Keltner Channel Volatility Squeeze using both the BBW or %B or other volatility squeeze indicators or a combination of all of them.
Also, I encourage people to post their top parameters for the BBW and %B to filter the Keltner Squeeze in the comments or to send me them by chat relative to this indicator.
Half the battle is making the indicator, while the other half is tuning the parameters.
The current parameters are one of the least aggressive, and act as a mild filter.
Note: You can also change the threshold for RVI top and bottom.
And this work builds on my last indicator:
If you have ideas on this work or have ideas on potential combinations please message me, I always want to learn or get perspective on how it can be improved.
Sharing is how we get better (Parameter tuning, ideas, discussion)
I don’t reinvent the wheel, just trying to make the wheel better.
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.
Compare Crypto Bollinger Bands//This is not financial advice, I am not a financial advisor.
//What are volatility tokens?
//Volatility tokens are ERC-20 tokens that aim to track the implied volatility of crypto markets.
//Volatility tokens get their exposure to an asset’s implied volatility using FTX MOVE contracts.
//There are currently two volatility tokens: BVOL and IBVOL.
//BVOL targets tracking the daily returns of being 1x long the implied volatility of BTC
//IBVOL targets tracking the daily returns of being 1x short the implied volatility of BTC.
/////////////////////////////////////////////////////////////////
CAN USE ON ANY CRYPTO CHART AS BINANCE:BTCUSD is still the most dominant crypto, positive volatility for BTC is positive for all.
/////////////////////////////////////////////////////////////////
//The Code.
//The blue line (ChartLine) is the current chart plotted on in Bollinger
//The red line (BVOLLine) plots the implied volatility of BTC
//The green line (IBVOLLine) plot the inverse implied volatility of BTC
//The orange line (TOTALLine) plots how well the crypto market is performing on the Bolling scale. The higher the number the better.
//There are 2 horizontal lines, 0.40 at the bottom & 0.60 at the top
/////////To Buy
//1. The blue line (ChartLine) must be higher than the green line (IBVOLLine)
//2. The green line (IBVOLLine) must be higher than the red line (BVOLLine)
//3. The red line (BVOLLine) must be less than 0.40 // This also acts as a trendsetter
//4. The orange line (TOTALLine) MUST be greater than the red line. This means that the crypto market is positive.
//5.IF THE BLUE LINE (ChartLine) IS GREATER THAN THE ORANGE LINE (TOTALLine) IT MEANS YOUR CRYPTO IS OUTPERFOMING THE MARKET {good for short term explosive bars}
//6. If the orange line (TOTALLine) is higher than your current chart, say BTCUSD. And BTC is going up to. It just means BTC is going up slowly. it's fine as long as they are moving in the same position.
//5. I use this on the 4hr, 1D, 1W timeframes
///////To Exit
//1.If the blue line (ChartLine) crosses under the green line (IBVOLLine) exit{ works best on 4hr,1D, 1W to avoid fakes}
//2.If the red line crosses over the green line when long. {close positions, or watch positions} It means negative volatility is wining
Volatility Bands by DGTVolatility represents how large an asset's prices swing around the mean price, the degree of variation of a trading price over time, and is commonly measured with beta (β) coefficients, standard deviations (σ) of returns where tools such as Average True Range, Bollinger Bands, Keltner Channel, Squeeze Indicator, etc presents volatility concept
Volatility often refers to the amount of uncertainty or risk related to the size of changes in a security's value. The higher the volatility, the riskier the security - the price of the security can change dramatically over a short time period in either direction. A lower volatility - security's value does not fluctuate dramatically, and tends to be more steady
This study, Volatility Bands , attempts to present a way to measure and visualize volatility , using standard deviations (σ) and average true range indicator, and aims to point out areas that might indicate potential trading opportunities
I will try to explain the usage with examples,
same setup with different option selected
as you may observe from the examples different setting may have advantages and disadvantages over one another, it is recommended to verify a trading setup with different available options.
Additionally, It is recommended to use this indicator in conjunction with other technical indicators, or verify using chart/candle patterns. Below is an usage example using in conjunction with other indicator, in the given example “Neglected Volume by DGT” is selected
Similarities and Differences
Bollinger Bands depicts two standard deviations above and below a simple moving average, and Keltner Channel depicts two times average true range (ATR) above and below an exponential moving average
Volatility Bands study combines the approach of both Bollinger Bands and Keltner Channel, with different settings and different visualization
Default settings are one standard deviations and one time average true range (ATR) above and below 13 period exponential moving average. Setting can be adjusted by users but let me remind all testes are performed with the default settings.
Mathematically expressed as
Upper band area between “ema + stdev” and “ema + atr”
Lower band area between “ema – stdev” and “ema – atr”
A different display is added with the inspiration I get from one of the @quantgym ‘s study, many thanks @quantgym 😉
When difference band display is selected the study will reflect the area between “ema + stdev – atr” and “ema – stdev + atr”. As shown in the examples above
Note: standard deviation calculation can be adjusted based on price action or its moving average.
Other differentiation between BB and KC is with V-BANDS mostly we look for trade opportunities when price action move out of the bands and in most cases we assume market is consolidating when the price action is within the bands
The other indicator that presents similarities to Volatility Bands is Squeeze Indicator, which measures the relationship between Bollinger Bands and Keltner's Channels to help identify consolidations and signal when prices are likely to break out. Mainly Volatility Bands is different version of Squeeze indicator, in fact the purpose is almost same but visualization is completely different. Additionally Volatility Bands Offers trading opportunities whereas Squeeze indicator only presents market states unless a momentum indicator is adapted to Squeeze indicator.
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
ATR based Pivots mcbwHey everyone this is an exciting new script I have prepared for you.
I was reading an old forex bulletin article some time ago when I came across this: solar.murty.net (or you can download the full bulletin with lots of other good articles here: www.forexfactory.com).
You can already buy this for metatrader (www.mql5.com) so I figured to make it for free for tradingview.
This bulletin suggested that you can reasonably predict daily volatility by adding or subtracting multiples of the daily ATR to the daily opening. Using this you can choose multiples to use as price targets and alternatively as stop losses. For example, if you already have a sense of market direction you can buy at market open place a stop loss at - 1 daily ATR and a profit target at + 3 ATRs for a risk to reward ratio of 3. If you are looking for smaller/quicker moves with a ratio of 3 you can have a stop loss at -0.25 ATR and a take profit at +0.75 ATR.
Alternatively this article also suggests to use this method to catch volatility breakouts. If price is higher than the + 1 ATR area then you can safely assume it will be going to the +2 ATR area so you can put a buy stop at + 1 ATR with a profit target at + 2 ATR with a stop loss at +0.5 ATR to catch a volatility breakout with a risk to reward ratio of 2!
Even further there are methods that you can use with ATRs of multiple window sizes, for example by opening two copies of this indicator and measuring recent volatility with a 1 week window and long term volatility within a 1 month window. If the short term volatility is crossing the long term volatility then there is a high probability chance that even more price movement will occur.
However I have found that this method is good for more than daily volatility , it can also be used to measure weekly volatility , and monthly volatility and use these multiples as good long term price targets.
To select if you want daily, weekly, or monthly values of the ATR of volatility you're using go to the settings and click on the options in the "Opening period". The default window of the ATR here is 14 periods, but you can change this if you want to in "ATR period". Most importantly you are able to select which multiples of the ATR you would like to use in the settings in "ATR multiple 1" which is the green line, "ATR multiple 2" which is the blue line, and "ATR multiple 3" which is the purple line. You can select any values you want to put in these, the choice of 0.25, 0.5, and 1 is not special, some people use fibonacci numbers here or simply 0.33, 0.66, and 0.99.
Repainting issue: This script uses the daily value of the Average True Range (ATR), which measures the volatility that is happening today. If price becomes more volatile then the value of the ATR can increase throughout the day, but it can never decrease. What this means is that the ATR based pivots are able to expand away from the opening price, which should not affect the trades that you take based on these areas. If you base your take profit on one of these ATR multiples and the daily volatility increase this means that your take profit area will be closer to your entry than the ATR multiple. Meaning that your trades will be more conservative.
While this all may sound very technical it is super intuitive, throw this on your chart and play around with it :)
Happy trading!
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.
