GKD-V Volatility Ratio [Loxx]Giga Kaleidoscope Volatility Ratio is a Volatility/Volume module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is an NNFX algorithmic trading strategy?
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend (such as "Baseline" shown on the chart above)
3. Confirmation 1 - a technical indicator used to identify trends. This should agree with the "Baseline"
4. Confirmation 2 - a technical indicator used to identify trends. This filters/verifies the trend identified by "Baseline" and "Confirmation 1"
5. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown.
6. Exit - a technical indicator used to determine when a trend is exhausted.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 module (Confirmation 1/2, Numbers 3 and 4 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 5 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 6 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Volatility Ratio as shown on the chart above
Confirmation 1: Vortex
Confirmation 2: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Now that you have a general understanding of the NNFX algorithm and the GKD trading system. Let's go over what's inside the GKD-V Volatility Ratio itself.
What is Volatility Ratio?
Volatility Ratio is a comparison between volatility and its moving average. This indicator includes 11 different types of volatility as well as 63 different moving averages.
You can read about the moving average types here:
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Signals
1. Traditional: The traditional signal is volatility is either high or low. This is non-directional. When the Volatility Ratio is above 1, then there is enough volatility to trade long or short. This signal type has a bar risings option that requires that the Volatility Ratio is not only above 1 but also rising for the last XX bars.
2. Crossing: This is experimental. When a cross-up above 1 or cross-down below one occurs then, and only then, is there enough volatility to trade long or short. This is also non-directional.
3. Both Traditional and Crossing
X-bar Rule
If a signal registers XX bars ago, then the signal is still valid. This is an optional feature.
Other things to note
The GKD trading system requires that a GKD-V indicator be present in the indicator chain, but the GKD-V indicator doesn't need to be active. You can turn on/off the Volatility Ratio as you wish so you can backtest your trading strategy with the filter on or off.
Additional features will be added in future releases.
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Pesquisar nos scripts por "Volatility"
Musashi_Slasher (Mometum+Volatility)--- Musashi Slasher (Momentum + Volatility ) ---
This tool was designed to fit my particular trading style and personal theories about the "Alchemy of the markets".
Velocity
This concept will be represented by the light blue and gray lines, a fast RSI (11 periods Relative Strength Index ), and a slow one ( RSI 14 periods as Wilder's half-cycle recommendation).
Note: Regular and hidden divergences will be plotted to help spot interesting spots and help with timing.
- Regular divergences will hint at a slowdown in price action.
- Hidden divergences will hint at a continuation as energy stored as some type of potential energy ready to be released violently. It is also referred ad 'The Slingshot'
Momentum
To understand Momentum, we must know that in physics Momentum = mass * velocity
We will understand mass as the mass of money of the market, which is found in the volume. To represent this concept a colored cloud will be plotted, this area will be given by MFI (13 periods Money Flow Index) and VRSI (Volume RSI ), when MFI is above VRSI will be colored dark blue, else red.
Note: Regular and hidden divergences on MFI will be plotted.
Volatility
The key to making this Alchemic theory work is to understand the "Transmutation" of the volatility which will be plotted by a multicolor line which will be blue in periods of low volatility and Red in periods of high volatility. I like to see these states as 'Ice' and 'Magma', as some periods the volatility just freezes, giving you hints that maybe a big move can be approaching, and at some points is just burning hot. Something I like about this indicator is that is trend agnostic. The line is named BBWP ( Bollinger Bands Width Percentile), as it calculates the width of the Bollinger bands (13 periods) and plot it as a percentile.
Finally, we will study the volatility of the volume, plotted as the red and purple mountains at the bottom of the indicator. This will complement and confirm the information provided by the Velocity-Momentum concepts.
Final Note
This indicator will only help identify interesting moments in the market, it's very powerful if used correctly, but it might be difficult to read in the beginning. It won't give "signals", as it is for understanding different dimensions in the market, I use it as it fits perfectly my trading strategies and tactics.
Best!
Musashi Alchemist
Volatility State Index [Interakktive]The Volatility State Index (VSI) classifies market volatility into three behavioral states: Expansion, Decay, and Transition. It answers one question visually: Is volatility supporting price movement, withdrawing, or unstable?
Unlike traditional volatility indicators that show levels or bands, VSI diagnoses the current volatility regime so traders can adapt their approach accordingly.
█ WHAT IT DOES
• Classifies volatility into three states: Expansion (teal), Decay (grey), Transition (amber)
• Measures volatility momentum as a percentage rate-of-change
• Applies stability filtering to detect unstable/choppy conditions
• Uses persistence logic to prevent state flickering
• Exports state data for use in alerts and strategies
█ WHAT IT DOES NOT DO
• NO buy/sell signals
• NO entry/exit recommendations
• NO alerts (v1 is diagnostic only)
• NO performance claims
This is a volatility diagnostic tool, not a trading system.
█ HOW IT WORKS
The VSI processes volatility through a five-stage pipeline:
STAGE 1 — Base Volatility
Calculates ATR as the foundation for volatility measurement.
STAGE 2 — Smoothing
Applies EMA smoothing to reduce noise in the volatility series.
STAGE 3 — Volatility Momentum
Computes the percentage rate-of-change of smoothed volatility:
Volatility Momentum (%) = ((Current ATR - Previous ATR) / Previous ATR) × 100
Positive values indicate expanding volatility; negative values indicate contracting volatility.
STAGE 4 — Stability Filter
Tracks how frequently volatility momentum changes direction. Frequent sign changes indicate unstable, choppy conditions.
Stability Score = 1 - (Average Flip Rate)
Low stability forces the Transition state regardless of momentum level.
STAGE 5 — State Classification
Combines momentum thresholds and stability to determine the final state:
• Expansion: Momentum ≥ +5% (default threshold)
• Decay: Momentum ≤ -5% (default threshold)
• Transition: Between thresholds OR low stability
A persistence filter requires states to hold for multiple bars before confirming, preventing visual noise.
█ INTERPRETATION
EXPANSION (Teal)
Volatility is increasing in a sustained way. Price moves are becoming larger.
What it suggests:
• Breakouts are more likely to follow through
• Stops may need wider placement
• Trend-following approaches tend to work better
• Mean-reversion weakens
DECAY (Grey)
Volatility is decreasing. Price is compressing into tighter ranges.
What it suggests:
• Breakouts are more likely to fail
• Ranges tend to hold
• Trend-following underperforms
• Mean-reversion strengthens
TRANSITION (Amber)
Volatility behavior is unclear or unstable. This is NOT neutral — it is uncertainty.
What it suggests:
• Mixed signals — one bar huge, next bar dead
• Higher whipsaw risk
• Reduced conviction in either direction
• Consider waiting for clarity
The key insight: Amber is a warning, not a middle ground. It appears when volatility cannot decide what it wants to do.
█ VISUAL DESIGN
The indicator uses a state-first histogram design:
• Histogram height shows volatility momentum percentage
• Histogram color shows the classified state
• Zero line provides visual anchor
• Optional momentum line for confirmation
• Optional background tint (default OFF for clean charts)
The visual hierarchy prioritizes instant state recognition. A trader should understand the volatility environment in under one second without reading numbers.
█ INPUTS
Core Settings
• ATR Length: Base volatility measurement period (default: 14)
• Smoothing Length: EMA smoothing applied to ATR (default: 10)
• Momentum Length: Rate-of-change lookback (default: 10)
State Classification
• Expansion Threshold (%): Momentum above this = Expansion (default: 5.0)
• Decay Threshold (%): Momentum below this = Decay (default: -5.0)
• Persistence Bars: Bars required to confirm state change (default: 3)
• Stability Lookback: Window for stability calculation (default: 20)
• Stability Threshold: Below this = forced Transition (default: 0.5)
Visual Settings
• Show State Histogram: Toggle main display (default: ON)
• Show Momentum Line: Thin confirmation line (default: OFF)
• Show Zero Line: Baseline reference (default: ON)
• Show Background Tint: Subtle state coloring (default: OFF)
█ DATA WINDOW EXPORTS
When enabled, the following values are exported:
• ATR (Raw)
• ATR (Smoothed)
• Volatility Momentum (%)
• Stability Score (0-1)
• State (-1/0/1): Decay = -1, Transition = 0, Expansion = 1
• Is Expansion (0/1)
• Is Decay (0/1)
• Is Transition (0/1)
These exports allow VSI to be used as a filter in Pine Script strategies or alert conditions.
█ ORIGINALITY
While ATR and volatility indicators are common, VSI is original because it:
1. Classifies volatility into behavioral states rather than showing raw levels
2. Applies momentum analysis to volatility itself (rate-of-change of ATR)
3. Uses stability filtering to detect genuinely unstable conditions
4. Implements persistence logic to prevent state flickering
5. Provides a state-first visual design optimized for instant recognition
VSI is state-first: it classifies volatility regimes (Expansion/Decay/Transition) rather than plotting volatility level alone, using momentum and stability to reduce false regime reads.
This is not a modified ATR or Bollinger Band — it is a volatility regime classifier.
█ SUITABLE MARKETS
Works on: Stocks, Futures, Forex, Crypto
Timeframes: All timeframes — state classification adapts accordingly
Best on: Instruments with consistent volatility patterns
█ RELATED
• Market Efficiency Ratio — measures price path efficiency
• Effort-Result Divergence — compares volume effort to price result
█ DISCLAIMER
This indicator is for educational purposes only. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own analysis before making trading decisions.
Volatility Cycle IndicatorThe Volatility Cycle Indicator is a non-directional trading tool designed to measure market volatility and cycles based on the relationship between standard deviation and Average True Range (ATR). In the Chart GBPAUD 1H time frame you can clearly see when volatility is low, market is ranging and when volatility is high market is expanding.
This innovative approach normalizes the standard deviation of closing prices by ATR, providing a dynamic perspective on volatility. By analyzing the interaction between Bollinger Bands and Keltner Channels, it also detects "squeeze" conditions, highlighting periods of reduced volatility, often preceding explosive price movements.
The indicator further features visual aids, including colored zones, plotted volatility cycles, and highlighted horizontal levels to interpret market conditions effectively. Alerts for key events, such as volatility crossing significant thresholds or entering a squeeze, make it an ideal tool for proactive trading.
Key Features:
Volatility Measurement:
Tracks the Volatility Cycle, normalized using standard deviation and ATR.
Helps identify periods of high and low volatility in the market.
Volatility Zones:
Colored zones represent varying levels of market volatility:
Blue Zone: Low volatility (0.5–0.75).
Orange Zone: Transition phase (0.75–1.0).
Green Zone: Moderate volatility (1.0–1.5).
Fuchsia Zone: High volatility (1.5–2.0).
Red Zone: Extreme volatility (>2.0).
Squeeze Detection:
Identifies when Bollinger Bands contract within Keltner Channels, signaling a volatility squeeze.
Alerts are triggered for potential breakout opportunities.
Visual Enhancements:
Dynamic coloring of the Volatility Cycle for clarity on its momentum and direction.
Plots multiple horizontal levels for actionable insights into market conditions.
Alerts:
Sends alerts when the Volatility Cycle crosses significant levels (e.g., 0.75) or when a squeeze condition is detected.
Non-Directional Nature:
The indicator does not predict the market's direction but rather highlights periods of potential movement, making it suitable for both trend-following and mean-reversion strategies.
How to Trade with This Indicator:
Volatility Squeeze Breakout:
When the indicator identifies a squeeze (volatility compression), prepare for a breakout in either direction.
Use additional directional indicators or chart patterns to determine the likely breakout direction.
Crossing Volatility Levels:
Pay attention to when the Volatility Cycle crosses the 0.75 level:
Crossing above 0.75 indicates increasing volatility—ideal for trend-following strategies.
Crossing below 0.75 signals decreasing volatility—consider mean-reversion strategies.
Volatility Zones:
Enter positions as volatility transitions through key zones:
Low volatility (Blue Zone): Watch for breakout setups.
Extreme volatility (Red Zone): Be cautious of overextended moves or reversals.
Alerts for Proactive Trading:
Configure alerts for squeeze conditions and level crossings to stay updated without constant monitoring.
Best Practices:
Pair the Volatility Cycle Indicator with directional indicators such as moving averages, trendlines, or momentum oscillators to improve trade accuracy.
Use on multiple timeframes to align entries with broader market trends.
Combine with risk management techniques, such as ATR-based stop losses, to handle volatility spikes effectively.
Vola2vola Volatility indicatorHello everyone!
For those who remember vola2vola volatility script, we are excited to bring it back within the Myfractalrange Tradingview account!
As you know, Volatility is very important to assets and many people use it to trade. This tool automate the calculation of the volatility of every asset as well as provide an estimated value of its "Trend" and "Trade".
The idea in this script is to allow users to have an idea of the current volatility regime of the asset he is monitoring: Is its volatility Bullish or Bearish Trend, Bearish or Bullish Trade? Is its volatility compressed to a previous minimum value? Is it about to experience a spike in volatility? Let's dig together into how this tool works and how you could integrate it into your trading shall we?
What are the data provided by the script, let see one by one:
- Volatility: The value of what vola2vola calls the "synthetic" volatility of the asset is calculated using a custom formula based on the VIXFIX formula. Default colour is blue
- Trade : Trade is generated using an arbitrary and fixed look back period, it acts as a short-term trend. It will give the user the possibility to know if the volatility of the asset is still trending short-term or not. Default colour is black
- Trend: Trend is also generated using an arbitrary and fixed look back period (20 times the one used for Trade), it acts as a longer-term trend. It works the same way as Trade and will give the user the possibility to know if the volatility of the asset is trending a longer-term basis or not. Default colours are: red when the Trend of the volatility of the asset is Bearish and green when the Trend of the volatility of the asset is Bullish
- 52-weeks high & low: Based on the highest and lowest value of Volatility in the past 52 weeks, a 52-weeks high and a 52-weeks low will be marked. These values usually acts as Resistance and Support for volatility. Default colour is black and they are in dotted lines
Here are some of the questions you need to know the answer to before using this script:
- How do you define a "Bullish/Bearish volatility Trade"? Volatility is Bullish Trade is when Volatility is above Trade and it is Bearish Trade when volatility is below Trade
- How do you define a "Bullish/Bearish volatility Trend"? Volatility is Bullish Trend is when Volatility is above Trend and it is Bearish Trend when volatility is below Trend
- On which time frame should i use this script? You want to use the Daily time frame. Although, for short term moves in the volatility space, users could monitor the Hourly timeframe
Understanding the volatility of an asset, along with the bullish or bearish nature of its Trade and Trend, is crucial for investors. Assets with decreasing volatility tend to appreciate in value, while those with increasing volatility tend to depreciate. Therefore, we recommend investors be aware of the volatility situation of the asset they are holding in their portfolio.
Here are the different scenarios that you will encounter on a Daily timeframe and how to interpret them:
- Volatility is below Trade & Trend and Volatility is Bearish Trade and Trend: It is the most Bullish set up for the price of an asset
- Volatility is above Trade & Trend and Volatility is Bullish Trade and Trend: It is the most Bearish set up for the price of an asset
- Any other set up suggests uncertainty, caution is therefore recommended
These are some cases that you could experience while using this script:
1) Bearish Volatility set up on a daily timeframe:
In this example using SPY, when its Volatility is Bearish Trend on a daily timeframe, the price of SPY tends to appreciate
2) Bullish Volatility set up on a daily timeframe:
In this example using SPY, when its Volatility is Bullish Trend on a daily timeframe, the price of SPY tends to depreciate
We hope that you will find these explanations useful, please contact us by private message for access.
Enjoy!
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorised. This script is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. Myfractalrange is not responsible for any losses you may incur. Please invest wisely.
TradeChartist Volatility Trader ™TradeChartist Volatility Trader is a Price Volatility based Trend indicator that uses simple to visualize Volatility steps and a Volatility Ribbon to trade volatility breakouts and price action based on lookback length.
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Features of ™TradeChartist Volatility Trader
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The Volatility steps consists of an Upper band, a Lower band and a Mean price line that are used for detecting the breakouts and also used in plotting the Volatility Ribbon based on the price action. The Mean Line is colour coded based on Bull/Bear Volatility and exhaustion based on Price action trend.
In addition to the system of Volatility Steps and Volatility Ribbon, ™TradeChartist Volatility Trader also plots Bull and Bear zones based on high probability volatility breakouts and divides the chart into Bull and Bear trade zones.
Use of External Filter is also possible by connecting an Oscillatory (like RSI, MACD, Stoch or any Oscillator) or a non-Oscillatory (Moving Average, Supertrend, any price scale based plots) Signal to confirm the Bull and Bear Trade zones. When the indicator detects the Volatility breakouts, it also checks if the connected external signal agrees with the trend before generating the Bull/Bear entries and plotting the trade zones.
Alerts can be created for Long and Short entries using Once per bar close .
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Note:
Higher the lookback length, higher the Risk/Reward from the trade zones.
This indicator does not repaint , but on the alert creation, a potential repaint warning would appear as the script uses security function. Users need not worry as this is normal on scripts that employs security functions. For trust and confidence using the indicator, users can do bar replay to check the plots/trade entries time stamps to make sure the plots and entries stay in the same place.
™TradeChartist Volatility Trader can be connected to ™TradeChartist Plug and Trade to generate Trade Entries, Targets etc by connecting Volatility Trader's Trend Identifier as Oscillatory Signal to Plug and Trade.
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Best Practice: Test with different settings first using Paper Trades before trading with real money
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This is not a free to use indicator. Get in touch with me (PM me directly if you would like trial access to test the indicator)
Premium Scripts - Trial access and Information
Trial access offered on all Premium scripts.
PM me directly to request trial access to the scripts or for more information.
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Specter Alpha-Omega Volatility Index™Meet the Alpha-Omega Volatility custom indicator by Specter
This premium volatility indicator uses a series of models to compare historical volatility, and by using a series of noise reduction techniques, it only gives you the very best signals. This indicator shows you aggressive reversals, which are often the most profitable.
The customization options already come with pre-sets, and it's as simple as one click. It comes with Aggressive, Moderate, Conservative and Ultra Conservative behaviors filters.
Also, it offers an interest zone indicator so you can start paying attention to the chart before it happens when trading extra volatile stocks timing is crucial and you want to be ready before the action begins.
The way you use it is pretty simple, you look for divergences. When you have a bullish movement, and you see high negative volatility appearing in the Alpha-Omega indicator, it means a strong reversal/spike is coming. The same goes for bearish reversals, just the opposite logic. You also get an extra layer of confirmation which is the Alpha/Omega characters; they only appear with the most robust volatility prediction. It's up to your trading strategy to decide how conservative you are and which signals you will follow.
It works on any market/security/asset/timeframe.
Ready to ride some spikes?
Volatility-Adjusted Momentum Oscillator (VAMO)Concept & Rationale: This indicator combines momentum and volatility into one oscillator. The idea is that a price move accompanied by high volatility has greater significance. We use Rate of Change (ROC) for momentum and Average True Range (ATR) for volatility, multiplying them to gauge “volatility-weighted momentum.” This concept is inspired by the Weighted Momentum & Volatility Indicator, which multiplies normalized ROC and ATR values. The result is shown as a histogram oscillating around zero – rising green bars indicate bullish momentum, while falling red bars indicate bearish momentum. When the histogram crosses above or below zero, it provides clear buy/sell signals. Higher magnitude bars suggest a stronger trend move. Crypto markets often see volatility spikes preceding big moves, so VAMO aims to capture those moments when momentum and volatility align for a powerful breakout.
Key Features:
Momentum-Volatility Fusion: Measures momentum (price ROC) adjusted by volatility (ATR). Strong trends register prominently only when price change is significant and volatility is elevated.
Intuitive Histogram: Plotted as a color-coded histogram around a zero line – green bars above zero for bullish trends, red bars below zero for bearish. This makes it easy to visualize trend strength and direction at a glance.
Clear Signals: A cross above 0 signals a buy, and below 0 signals a sell. Traders can also watch for the histogram peaking and then shrinking as an early sign of a trend reversal (e.g. bars switching from growing to shrinking while still positive could mean bullish momentum is waning).
Optimized for Volatility: Because ATR is built-in, the oscillator naturally adapts to crypto volatility. In calm periods, signals will be smaller (reducing noise), whereas during volatile swings the indicator accentuates the move, helping predict big price swings.
Customization: The lookback period is adjustable. Shorter periods (e.g. 5-10) make it more sensitive for scalping, while longer periods (20+) smooth it out for swing trading.
How to Use: When VAMO bars turn green and push above zero, it indicates bullish momentum with strong volatility – a cue that price is likely to rally in the near term. Conversely, red bars below zero signal bearish pressure. For example, if a coin’s price has been flat and then VAMO spikes green above zero, it suggests an explosive upward move is brewing. Traders can enter on the zero-line cross (or on the first green bar) and consider exiting when the histogram peaks and starts shrinking (signaling momentum slowdown). In sideways markets, VAMO will hover near zero – staying out during those low-volatility periods helps avoid false signals. This indicator’s strength is catching the moment when a quiet market turns volatile in one direction, which often precedes the next few candlesticks of sustained movement.
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel.
█ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values.
█ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical Volatility Rank, and Historical Volatility Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models.
Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias.
Pros:
• Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator.
Cons:
• The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.
• Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.
• Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator:
• Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.)
Garman-Klass Estimator:
• Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.
• The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility.
Rogers-Satchell Estimator:
• Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.
• The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.
• Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension:
• Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.
• The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator:
• The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.
• This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.
• 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV.
EWMA Estimator:
• EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.
• EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.
• RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.
• However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH.
Adjusted Mean Absolute Deviation Estimator:
• This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.
• It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.
• The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.
• In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility.
Adjusted Median Absolute Deviation Estimator:
• This is another estimator that does not use standard deviation to measure volatility.
• Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.
• The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
█ FEATURES
• You can select the volatility estimator models in the Volatility Model input
• Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.
• Alpha is used to adjust the Yang Zhang volatility estimator value.
• Percentile Length is used to Adjust Percentile coloring lookbacks.
• The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.
• When percentile coloring is off, it won’t show the gradient color.
• You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.
• When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
• You can choose the plot style, such as lines, columns, areas in the plotstyle input.
• When the show information panel is on, a small panel will display on the right.
• The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value.
█ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move).
█ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Historical Volatility (adjustable time period)Historical Volatility with Adjustable Time Period and Moving Average
This indicator calculates the historical volatility of an asset within a user-defined date range. Volatility is a measure of the dispersion of returns and is commonly used to assess the risk and potential price fluctuations of an asset.
How It Works
User-Defined Date Range: You can specify the start and end dates to focus on a particular period for volatility calculation. This is useful for analyzing specific historical events or trends within a defined timeframe.
Daily Returns Calculation: The script calculates the daily returns as the percentage change between the current close price and the previous close price. This percentage change is essential for determining the asset's volatility.
Volatility Calculation: The historical volatility is computed as the standard deviation of the daily returns over a specified period. The standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values.
Moving Average: An optional feature allows you to plot a moving average of the volatility. You can customize the type (SMA, EMA, WMA, VWMA) and the period of the moving average, helping to smooth out the volatility data and identify trends.
Indicator Settings
Start Date: Select the beginning date of the period for which you want to calculate volatility.
End Date: Select the end date of the period.
Period: Set the number of bars (days) over which to calculate the average volatility.
Show Moving Average: Toggle to display the moving average of the volatility.
Moving Average Period: Define the length of the moving average.
Moving Average Type: Choose the type of moving average: Simple (SMA), Exponential (EMA), Weighted (WMA), or Volume-Weighted (VWMA).
How to Use
Configure Date Range: Set the start and end dates to focus on the specific historical period you are interested in.
Adjust Period for Volatility Calculation: Select the period over which you want to calculate the average volatility. A shorter period will be more sensitive to recent price changes, while a longer period will provide a more smoothed view.
Enable and Configure Moving Average: If desired, enable the moving average and select the type and period that best fits your analysis style.
Example Use Cases
Market Analysis: Identify periods of high or low volatility to assess market conditions.
Risk Management: Use historical volatility to evaluate the risk associated with a particular asset.
Event Impact: Analyze how specific events within the selected date range affected the asset's volatility.
By providing these functionalities, this indicator is a valuable tool for traders looking to understand and analyze the volatility of assets over custom time periods with the flexibility of adding a moving average for trend analysis.
[blackcat] L1 Dynamic Volatility IndicatorThe volatility indicator (Volatility) is used to measure the magnitude and instability of price changes in financial markets or a specific asset. This thing is usually used to assess how risky the market is. The higher the volatility, the greater the fluctuation in asset prices, but brother, the risk is also relatively high! Here are some related terms and explanations:
- Historical Volatility: The actual volatility of asset prices over a certain period of time in the past. This thing is measured by calculating historical data.
- Implied Volatility: The volatility inferred from option market prices, used to measure market expectations for future price fluctuations.
- VIX Index (Volatility Index): Often referred to as the "fear index," it predicts the volatility of the US stock market within 30 days in advance. This is one of the most famous volatility indicators in global financial markets.
Volatility indicators are very important for investors and traders because they can help them understand how unstable and risky the market is, thereby making wiser investment decisions.
Today I want to introduce a volatility indicator that I have privately held for many years. It can use colors to judge sharp rises and falls! Of course, if you are smart enough, you can also predict some potential sharp rises and falls by looking at the trend!
In the financial field, volatility indicators measure the magnitude and instability of price changes in different assets. They are usually used to assess the level of market risk. The higher the volatility, the greater the fluctuation in asset prices and therefore higher risk. Historical Volatility refers to the actual volatility of asset prices over a certain period of time in the past, which can be measured by calculating historical data; while Implied Volatility is derived from option market prices and used to measure market expectations for future price fluctuations. In addition, VIX Index is commonly known as "fear index" and is used to predict volatility in the US stock market within 30 days. It is one of the most famous volatility indicators in global financial markets.
Volatility indicators are very important for investors and traders because they help them understand market uncertainty and risk, enabling them to make wiser investment decisions. The L1 Dynamic Volatility Indicator that I am introducing today is an indicator that measures volatility and can also judge sharp rises and falls through colors!
This indicator combines two technical indicators: Dynamic Volatility (DV) and ATR (Average True Range), displaying warnings about sharp rises or falls through color coding. DV has a slow but relatively smooth response, while ATR has a fast but more oscillating response. By utilizing their complementary characteristics, it is possible to construct a structure similar to MACD's fast-slow line structure. Of course, in order to achieve fast-slow lines for DV and ATR, first we need to unify their coordinate axes by normalizing them. Then whenever ATR's yellow line exceeds DV's purple line with both curves rapidly breaking through the threshold of 0.2, sharp rises or falls are imminent.
However, it is important to note that relying solely on the height and direction of these two lines is not enough to determine the direction of sharp rises or falls! Because they only judge the trend of volatility and cannot determine bull or bear markets! But it's okay, I have already considered this issue early on and added a magical gradient color band. When the color band gradually turns warm, it indicates a sharp rise; conversely, when the color band tends towards cool colors, it indicates a sharp fall! Of course, you won't see the color band in sideways consolidation areas, which avoids your involvement in unnecessary trades that would only waste your funds! This indicator is really practical and with it you can better assess market risks and opportunities!
Smoothed Volatility Bands [CC]The Smoothed Volatility Bands were created by Sylvain Vervoort (Stocks and Commodities Sep 2020 pg 19) and this is a heavily customized version of regular Bollinger Bands that take volatility into account. Feel free to change the moving average since Vervoort recommended trying that out. Buy when the indicator line turns green and sell when it turns red.
Let me know if there are any other indicators you want me to publish!
Volume (Incremental) Weighted VOLATILITY BANDSDISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is following TradingView's regulations. Use of indicator and their code are published by Invitation Only for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED AS TESTING. The models included in the indicator have been taken from openly sources on the web, problems could occur at diverse data sceneries.
WHAT'S THIS...?
Work derived by previous own research for study:
The given indicator is another VWAP analysis tool that contains openly procedures for rolling out time sessions as in other TradingView scripts .
Some novelties are introduced in this version:
INCREMENTAL WEIGHTED STANDARD DEVIATION BANDS: The calculation on this script are strictly based in regard of University of Cambridge Computing Service, February 2009 paper by Tony Finch publicly found at people.ds.cam.ac.uk .
From the Abstract, he explain how to derive formulae for numerically stable calculation of the mean and standard deviation, which are also suitable for incremental on-line calculation. Then he generalize these formulae to weighted means and standard deviations. He unpick the difficulties that arise when generalizing further to normalized weights. Finally he shown that the exponentially weighted moving average is a special case of the incremental normalized weighted mean formula, and derive a formula for the exponentially weighted moving standard deviation.
VOLUME WEIGHTED VOLATILITY ADAPTIVE MOVING AVERAGE & BANDS: Taking the INCREMENTAL WEIGHTED STANDARD DEVIATION already described and taking a specified anchor or Rolling procedure for a VWAP, I derive the variance against the price to use it as VOLATILITY PROXY for a normalization lambda to plot a First Order Impulse Response Filter or Adaptive Average . This idea have it's roots derived from Chaiyuth Padungsaksawasdi & Robert T. Daigler paper entitled "Volume weighted volatility: empirical evidence for a new realised volatility measure".
NOTES:
This version DO NOT INCLUDE ALERTS.
This version DO NOT INCLUDE STRATEGY: Feedback are welcome.
DERIVED WORK:
Incremental calculation of weighted mean and variance by Tony Finch (fanf2@cam.ac.uk) (dot@dotat.at), 2009.
Volume weighted volatility: empirical evidence for a new realised volatility measure by Chaiyuth Padungsaksawasdi & Robert T. Daigler, 2018.
Multi-Timeframe VWAP by TradingView user @mortdiggidi
CHEERS!
@XeL_Arjona 2019.
Fear Volatility Gate [by Oberlunar]The Fear Volatility Gate by Oberlunar is a filter designed to enhance operational prudence by leveraging volatility-based risk indices. Its architecture is grounded in the empirical observation that sudden shifts in implied volatility often precede instability across financial markets. By dynamically interpreting signals from globally recognized "fear indices", such as the VIX, the indicator aims to identify periods of elevated systemic uncertainty and, accordingly, restrict or flag potential trade entries.
The rationale behind the Fear Volatility Gate is rooted in the understanding that implied volatility represents a forward-looking estimate of market risk. When volatility indices rise sharply, it reflects increased demand for options and a broader perception of uncertainty. In such contexts, price movements can become less predictable, more erratic, and often decoupled from technical structures. Rather than relying on price alone, this filter provides an external perspective—derived from derivative markets—on whether current conditions justify caution.
The indicator operates in two primary modes: single-source and composite . In the single-source configuration, a user-defined volatility index is monitored individually. In composite mode, the filter can synthesize input from multiple indices simultaneously, offering a more comprehensive macro-risk assessment. The filtering logic is adaptable, allowing signals to be combined using inclusive (ANY), strict (ALL), or majority consensus logic. This allows the trader to tailor sensitivity based on the operational context or asset class.
The indices available for selection cover a broad spectrum of market sectors. In the equity domain, the filter supports the CBOE Volatility Index ( CBOE:VIX VIX) for the S&P 500, the Nasdaq-100 Volatility Index ( CBOE:VXN VXN), the Russell 2000 Volatility Index ( CBOEFTSE:RVX RVX), and the Dow Jones Volatility Index ( CBOE:VXD VXD). For commodities, it integrates the Crude Oil Volatility Index ( CBOE:OVX ), the Gold Volatility Index ( CBOE:GVZ ), and the Silver Volatility Index ( CBOE:VXSLV ). From the fixed income perspective, it includes the ICE Bank of America MOVE Index ( OKX:MOVEUSD ), the Volatility Index for the TLT ETF ( CBOE:VXTLT VXTLT), and the 5-Year Treasury Yield Index ( CBOE:FVX.P FVX). Within the cryptocurrency space, it incorporates the Bitcoin Volmex Implied Volatility Index ( VOLMEX:BVIV BVIV), the Ethereum Volmex Implied Volatility Index ( VOLMEX:EVIV EVIV), the Deribit Bitcoin Volatility Index ( DERIBIT:DVOL DVOL), and the Deribit Ethereum Volatility Index ( DERIBIT:ETHDVOL ETHDVOL). Additionally, the user may define a custom instrument for specialized tracking.
To determine whether market conditions are considered high-risk, the indicator supports three modes of evaluation.
The moving average cross mode compares a fast Hull Moving Average to a slower one, triggering a signal when short-term volatility exceeds long-term expectations.
The Z-score mode standardizes current volatility relative to historical mean and standard deviation, identifying significant deviations that may indicate abnormal market stress.
The percentile mode ranks the current value against a historical distribution, providing a relative perspective particularly useful when dealing with non-normal or skewed distributions.
When at least one selected index meets the condition defined by the chosen mode, and if the filtering logic confirms it, the indicator can mark the trading environment as “blocked”. This status is visually highlighted through background color changes and symbolic markers on the chart. An optional tabular interface provides detailed diagnostics, including raw values, fast-slow MA comparison, Z-scores, percentile levels, and binary risk status for each active index.
The Fear Volatility Gate is not a predictive tool in itself but rather a dynamic constraint layer that reinforces discipline under conditions of macro instability. It is particularly valuable when trading systems are exposed to highly leveraged or short-duration strategies, where market noise and sentiment can temporarily override structural price behavior. By synchronizing trading signals with volatility regimes, the filter promotes a more cautious, informed approach to decision-making.
This approach does not assume that all volatility spikes are harmful or that market corrections are imminent. Rather, it acknowledges that periods of elevated implied volatility statistically coincide with increased execution risk, slippage, and spread widening, all of which may erode the profitability of even the most technically accurate setups.
Therefore, the Fear Volatility Gate acts as a protective mechanism.
Oberlunar 👁️⭐
(MVD) Meta-Volatility Divergence (DAFE) Meta-Volatility Divergence (MVD)
Reveal the Hidden Tension in Volatility.
The Meta-Volatility Divergence (MVD) indicator is a next-generation tool designed to expose the disagreement between multiple volatility measures—helping you spot when the market’s “volatility engines” are out of sync, and a regime shift or volatility event may be brewing.
What Makes MVD Unique?
Multi-Source Volatility Analysis:
Unlike traditional volatility indicators that rely on a single measure, MVD fuses four distinct volatility signals:
ATR (Average True Range): Captures the average range of price movement.
Stdev (Standard Deviation): Measures the dispersion of closing prices.
Range: The average difference between high and low.
VoVix: A proprietary “volatility of volatility” metric, quantifying the difference between fast and slow ATR, normalized by ATR’s own volatility.
Divergence Engine:
The core MVD line (yellow) represents the mean absolute deviation (MAD) of these volatility measures from their average. When the line is flat, all volatility measures are in agreement. When the line rises, it means the market’s volatility signals are diverging—often a precursor to regime shifts, volatility expansions, or hidden stress.
Dynamic Z-Score Normalization:
The MVD line is normalized as a Z-score, so you can easily spot when current divergence is rare or extreme compared to recent history.
Visual Clarity:
Yellow center line: Tracks the real-time divergence of volatility measures.
Green dashed thresholds: Mark the ±2.00 Z-score levels, highlighting when divergence is unusually high and action may be warranted.
Dashboard: Toggleable panel shows all key metrics (ATR, Stdev, VoVix, MVD Z) and your custom branding.
Compact Info Label : For mobile or minimalist users, a single-line summary keeps you informed without clutter.
What Makes The MVD line move?
- The MVD line rises when the included volatility measures (ATR, Stdev, Range, VoVix) are moving in different directions or at different magnitudes. For example, if ATR is rising but Stdev is falling, the line will move up, signaling disagreement.
- The line falls or flattens when all volatility measures are in sync, indicating a consensus in the market’s volatility regime.
- VoVix adds a unique dimension, making the indicator especially sensitive to sudden changes in volatility structure that most tools miss.
Inputs & Settings
ATR Length: Sets the lookback for ATR calculation. Shorter = more sensitive, longer = smoother.
Stdev Length: Sets the lookback for standard deviation. Adjust for your asset’s volatility.
Range Length: Sets the lookback for the average high-low range.
MVD Lookback: Controls the window for Z-score normalization. Higher values = more historical context, lower = more responsive.
Show Dashboard: Toggle the full dashboard panel on/off.
Show Compact Info Label: Toggle the mobile-friendly info line on/off.
Tip:
Adjust these settings to match your asset’s volatility and your trading timeframe. There is no “one size fits all”—tuning is key to extracting the most value from MVD.
How to make MVD work for you:
Threshold Crosses: When the MVD line crosses above or below the green dashed thresholds (±2.00), it signals that volatility measures are diverging more than usual. This is a heads-up that a volatility event, regime shift, or hidden market stress may be developing.
Not a Buy/Sell Signal: A threshold cross is not a direct buy or sell signal. It is an indication that the market’s volatility structure is changing. Use it as a filter, confirmation, or alert in combination with your own strategy and risk management.
Dashboard & Info Line: Use the dashboard for a full view of all metrics, or the info label for a quick glance—especially useful on mobile.
Chart: MNQ! on 5min frames
ATR: 14
StDev L: 11
Range L: 13
MDV LB: 13
Important Note
MVD is a market structure and volatility regime tool.
It is designed to alert you to potential changes in market conditions, not to provide direct trade entries or exits. Always combine with your own analysis and risk management.
Meta-Volatility Divergence:
See the market’s hidden tension. Anticipate the next wave.
For educational purposes only. Not financial advice. Always use proper risk management.
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Volatility with Sigma BandsOverview
The Volatility Analysis with Sigma Bands indicator is a powerful and flexible tool designed for traders who want to gain deeper insights into market price fluctuations. It calculates historical volatility within a user-defined time range and displays ±1σ, ±2σ, and ±3σ standard deviation bands, helping traders identify potential support, resistance levels, and extreme price behaviors.
Key Features
Multiple Volatility Band Displays:
±1σ Range (Yellow line): Covers approximately 68% of price fluctuations.
±2σ Range (Blue line): Covers approximately 95% of price fluctuations.
±3σ Range (Fuchsia line): Covers approximately 99% of price fluctuations.
Dynamic Probability Mode:
Toggle between standard normal distribution probabilities (68.2%, 95.4%, 99.7%) and actual historical probability calculations, allowing for more accurate analysis tailored to varying market conditions.
Highly Customizable Label Display:
The label shows:
Real-time volatility
Annualized volatility
Current price
Price ranges for each σ level
Users can adjust the label’s position and horizontal offset to prevent it from overlapping key price areas.
Real-Time Calculation & Visualization:
The indicator updates in real-time based on the selected time range and current market data, making it suitable for day trading, swing trading, and long-term trend analysis.
Use Cases
Risk Management:
Understand the distribution probabilities of price within different standard deviation bands to set more effective stop-loss and take-profit levels.
Trend Confirmation:
Determine trend strength or spot potential reversals by observing whether the price breaks above or below ±1σ or ±2σ ranges.
Market Sentiment Analysis:
Price movement beyond the ±3σ range often indicates extreme market sentiment, providing potential reversal opportunities.
Backtesting and Historical Analysis:
Utilize the customizable time range feature to backtest volatility during various periods, providing valuable insights for strategy refinement.
The Volatility Analysis with Sigma Bands indicator is an essential tool for traders seeking to understand market volatility patterns. Whether you're a day trader looking for precise entry and exit points or a long-term investor analyzing market behavior, this indicator provides deep insights into volatility dynamics, helping you make more confident trading decisions.
Rolling Volatility Indicator
Description :
The Rolling Volatility indicator calculates the volatility of an asset's price movements over a specified period. It measures the degree of variation in the price series over time, providing insights into the market's potential for price fluctuations.
This indicator utilizes a rolling window approach, computing the volatility by analyzing the logarithmic returns of the asset's price. The user-defined length parameter determines the timeframe for the volatility calculation.
How to Use :
Adjust the "Length" parameter to set the rolling window period for volatility calculation.
Ajust "trading_days" for the sampling period, this is the total number of trading days (usually 252 days for stocks and 365 for crypto)
Higher values for the length parameter will result in a smoother, longer-term view of volatility, while lower values will provide a more reactive, shorter-term perspective.
Volatility levels can assist in identifying periods of increased market activity or potential price changes. Higher volatility may suggest increased risk and potential opportunities, while lower volatility might indicate periods of reduced market activity.
Key Features :
Customizable length parameter for adjusting the calculation period and trading days such that it can also be applied to stock market or any markets.
Visual representation of volatility with a plotted line on the chart.
The Rolling Volatility indicator can be a valuable tool for traders and analysts seeking insights into market volatility trends, aiding in decision-making processes and risk management strategies.
[blackcat] L1 Volatility Quality Index (VQI)The Volatility Quality Index (VQI) is an indicator used to measure the quality of market volatility. Volatility refers to the extent of price changes in the market. VQI helps traders assess market stability and risk levels by analyzing price volatility. This introduction may be a bit abstract, so let me help you understand it with a comparative metaphor if you're not immersed in various technical indicators.
Imagine you are playing a jump rope game, and you notice that sometimes the rope moves fast and other times it moves slowly. This is volatility, which describes the speed of the rope. VQI is like an instrument specifically designed to measure rope speed. It observes the movement of the rope and provides a numerical value indicating how fast or slow it is moving. This value can help you determine both the stability of the rope and your difficulty level in jumping over it. With this information, you know when to start jumping and when to wait while skipping rope.
In trading, VQI works similarly. It observes market price volatility and provides a numerical value indicating market stability and risk levels for traders. If VQI has a high value, it means there is significant market volatility with relatively higher risks involved. Conversely, if VQI has a low value, it indicates lower market volatility with relatively lower risks involved as well. The calculation involves dividing the range by values obtained from calculating Average True Range (ATR) multiplied by a factor/multiple.
The purpose of VQI is to assist traders in evaluating the quality of market volatility so they can develop better trading strategies accordingly.
Therefore, VQI helps traders understand the quality of market volatility for better strategy formulation and risk management—just like adjusting your jumping style based on rope speed during jump-rope games; traders can adjust their trading decisions based on VQI values.
The calculation of VQI indicator depends on given period length and multiple factors: Period length is used to calculate Average True Range (ATR), while the multiple factor adjusts the range of volatility. By dividing the range by values and multiplying it with a multiple, VQI numerical value can be obtained.
VQI indicator is typically presented in the form of a histogram on price charts. Higher VQI values indicate better quality of market volatility, while lower values suggest poorer quality of volatility. Traders can use VQI values to assess the strength and reliability of market volatility, enabling them to make wiser trading decisions.
It should be noted that VQI is just an auxiliary indicator; traders should consider other technical indicators and market conditions comprehensively when making decisions. Additionally, parameter settings for VQI can also be adjusted and optimized based on individual trading preferences and market characteristics.
Uptrick: Volatility Reversion BandsUptrick: Volatility Reversion Bands is an indicator designed to help traders identify potential reversal points in the market by combining volatility and momentum analysis within one comprehensive framework. It calculates dynamic bands around a simple moving average and issues signals when price interacts with these bands. Below is a fully expanded description, structured in multiple sections, detailing originality, usefulness, uniqueness, and the purpose behind blending standard deviation-based and ATR-based concepts. All references to code have been removed to focus on the written explanation only.
Section 1: Overview
Uptrick: Volatility Reversion Bands centers on a moving average around which various bands are constructed. These bands respond to changes in price volatility and can help gauge potential overbought or oversold conditions. Signals occur when the price moves beyond certain thresholds, which may imply a reversal or significant momentum shift.
Section 2: Originality, Usefulness, Uniqness, Purpose
This indicator merges two distinct volatility measurements—Bollinger Bands and ATR—into one cohesive system. Bollinger Bands use standard deviation around a moving average, offering a baseline for what is statistically “normal” price movement relative to a recent mean. When price hovers near the upper band, it may indicate overbought conditions, whereas price near the lower band suggests oversold conditions. This straightforward construction often proves invaluable in moderate-volatility settings, as it pinpoints likely turning points and gauges a market’s typical trading range.
Yet Bollinger Bands alone can falter in conditions marked by abrupt volatility spikes or sudden gaps that deviate from recent norms. Intraday news, earnings releases, or macroeconomic data can alter market behavior so swiftly that standard-deviation bands do not keep pace. This is where ATR (Average True Range) adds an important layer. ATR tracks recent highs, lows, and potential gaps to produce a dynamic gauge of how much price is truly moving from bar to bar. In quieter times, ATR contracts, reflecting subdued market activity. In fast-moving markets, ATR expands, exposing heightened volatility on each new bar.
By overlaying Bollinger Bands and ATR-based calculations, the indicator achieves a broader situational awareness. Bollinger Bands excel at highlighting relative overbought or oversold areas tied to an established average. ATR simultaneously scales up or down based on real-time market swings, signaling whether conditions are calm or turbulent. When combined, this means a price that barely crosses the Bollinger Band but also triggers a high ATR-based threshold is likely experiencing a volatility surge that goes beyond typical market fluctuations. Conversely, a price breach of a Bollinger Band when ATR remains low may still warrant attention, but not necessarily the same urgency as in a high-volatility regime.
The resulting synergy offers balanced, context-rich signals. In a strong trend, the ATR layer helps confirm whether an apparent price breakout really has momentum or if it is just a temporary spike. In a range-bound market, standard deviation-based Bollinger Bands define normal price extremes, while ATR-based extensions highlight whether a breakout attempt has genuine force behind it. Traders gain clarity on when a move is both statistically unusual and accompanied by real volatility expansion, thus carrying a higher probability of a directional follow-through or eventual reversion.
Practical advantages emerge across timeframes. Scalpers in fast-paced markets appreciate how ATR-based thresholds update rapidly, revealing if a sudden price push is routine or exceptional. Swing traders can rely on both indicators to filter out false signals in stable conditions or identify truly notable moves. By calibrating to changes in volatility, the merged system adapts naturally whether the market is trending, ranging, or transitioning between these phases.
In summary, combining Bollinger Bands (for a static sense of standard-deviation-based overbought/oversold zones) with ATR (for a dynamic read on current volatility) yields an adaptive, intuitive indicator. Traders can better distinguish fleeting noise from meaningful expansions, enabling more informed entries, exits, and risk management. Instead of relying on a single yardstick for all market conditions, this fusion provides a layered perspective, encouraging traders to interpret price moves in the broader context of changing volatility.
Section 3: Why Bollinger Bands and ATR are combined
Bollinger Bands provide a static snapshot of volatility by computing a standard deviation range above and below a central average. ATR, on the other hand, adapts in real time to expansions or contractions in market volatility. When combined, these measures offset each other’s limitations: Bollinger Bands add structure (overbought and oversold references), and ATR ensures responsiveness to rapid price shifts. This synergy helps reduce noisy signals, particularly during sudden market turbulence or extended consolidations.
Section 4: User Inputs
Traders can adjust several parameters to suit their preferences and strategies. These typically include:
1. Lookback length for calculating the moving average and standard deviation.
2. Multipliers to control the width of Bollinger Bands.
3. An ATR multiplier to set the distance for additional reversal bands.
4. An option to display weaker signals when the price merely approaches but does not cross the outer bands.
Section 5: Main Calculations
At the core of this indicator are four important steps:
1. Calculate a basis using a simple moving average.
2. Derive Bollinger Bands by adding and subtracting a product of the standard deviation and a user-defined multiplier.
3. Compute ATR over the same lookback period and multiply it by the selected factor.
4. Combine ATR-based distance with the Bollinger Bands to set the outer reversal bands, which serve as stronger signal thresholds.
Section 6: Signal Generation
The script interprets meaningful reversal points when the price:
1. Crosses below the lower outer band, potentially highlighting oversold conditions where a bullish reversal may occur.
2. Crosses above the upper outer band, potentially indicating overbought conditions where a bearish reversal may develop.
Section 7: Visualization
The indicator provides visual clarity through labeled signals and color-coded references:
1. Distinct colors for upper and lower reversal bands.
2. Markers that appear above or below bars to denote possible buying or selling signals.
3. A gradient bar color scheme indicating a bar’s position between the lower and upper bands, helping traders quickly see if the price is near either extreme.
Section 8: Weak Signals (Optional)
For those preferring early cues, the script can highlight areas where the price nears the outer bands. When weak signals are enabled:
1. Bars closer to the upper reversal zone receive a subtle marker suggesting a less robust, yet still noteworthy, potential selling area.
2. Bars closer to the lower reversal zone receive a subtle marker suggesting a less robust, yet still noteworthy, potential buying area.
Section 9: Simplicity, Effectiveness, and Lower Timeframes
Although combining standard deviation and ATR involves sophisticated volatility concepts, this indicator is visually straightforward. Reversal bands and gradient-colored bars make it easy to see at a glance when price approaches or crosses a threshold. Day traders operating on lower timeframes benefit from such clarity because it helps filter out minor fluctuations and focus on more meaningful signals.
Section 10: Adaptability across Market Phases
Because both the standard deviation (for Bollinger Bands) and ATR adapt to changing volatility, the indicator naturally adjusts to various environments:
1. Trending: The additional ATR-based outer bands help distinguish between temporary pullbacks and deeper reversals.
2. Ranging: Bollinger Bands often remain narrower, identifying smaller reversals, while the outer ATR bands remain relatively close to the main bands.
Section 11: Reduced Noise in High-Volatility Scenarios
By factoring ATR into the band calculations, the script widens or narrows the thresholds during rapid market fluctuations. This reduces the amount of false triggers typically found in indicators that rely solely on fixed calculations, preventing overreactions to abrupt but short-lived price spikes.
Section 12: Incorporation with Other Technical Tools
Many traders combine this indicator with oscillators such as RSI, MACD, or Stochastic, as well as volume metrics. Overbought or oversold signals in momentum oscillators can provide additional confirmation when price reaches the outer bands, while volume spikes may reinforce the significance of a breakout or potential reversal.
Section 13: Risk Management Considerations
All trading strategies carry risk. This indicator, like any tool, can and does produce losing trades if price unexpectedly reverses again or if broader market conditions shift rapidly. Prudent traders employ protective measures:
1. Stop-loss orders or trailing stops.
2. Position sizing that accounts for market volatility.
3. Diversification across different asset classes when possible.
Section 14: Overbought and Oversold Identification
Standard Bollinger Bands highlight regions where price might be overextended relative to its recent average. The extended ATR-based reversal bands serve as secondary lines of defense, identifying moments when price truly stretches beyond typical volatility bounds.
Section 15: Parameter Customization for Different Needs
Users can tailor the script to their unique preferences:
1. Shorter lookback settings yield faster signals but risk more noise.
2. Higher multipliers spread the bands further apart, filtering out small moves but generating fewer signals.
3. Longer lookback periods smooth out market noise, often leading to more stable but less frequent trading cues.
Section 16: Examples of Different Trading Styles
1. Day Traders: Often reduce the length to capture quick price swings.
2. Swing Traders: May use moderate lengths such as 20 to 50 bars.
3. Position Traders: Might opt for significantly longer settings to detect macro-level reversals.
Section 17: Performance Limitations and Reality Check
No technical indicator is free from false signals. Sudden fundamental news events, extreme sentiment changes, or low-liquidity conditions can render signals less reliable. Backtesting and forward-testing remain essential steps to gauge whether the indicator aligns well with a trader’s timeframe, risk tolerance, and instrument of choice.
Section 18: Merging Volatility and Momentum
A critical uniqueness of this indicator lies in how it merges Bollinger Bands (standard deviation-based) with ATR (pure volatility measure). Bollinger Bands provide a relative measure of price extremes, while ATR dynamically reacts to market expansions and contractions. Together, they offer an enhanced perspective on potential market turns, ideally reducing random noise and highlighting moments where price has traveled beyond typical bounds.
Section 19: Purpose of this Merger
The fundamental purpose behind blending standard deviation measures with real-time volatility data is to accommodate different market behaviors. Static standard deviation alone can underreact or overreact in abnormally volatile conditions. ATR alone lacks a baseline reference to normality. By merging them, the indicator aims to provide:
1. A versatile dynamic range for both typical and extreme moves.
2. A filter against frequent whipsaws, especially in choppy environments.
3. A visual framework that novices and experts can interpret rapidly.
Section 20: Summary and Practical Tips
Uptrick: Volatility Reversion Bands offers a powerful tool for traders looking to combine volatility-based signals with momentum-derived reversals. It emphasizes clarity through color-coded bars, defined reversal zones, and optional weak signal markers. While potentially useful across all major timeframes, it demands ongoing risk management, realistic expectations, and careful study of how signals behave under different market conditions. No indicator serves as a crystal ball, so integrating this script into an overall strategy—possibly alongside volume data, fundamentals, or momentum oscillators—often yields the best results.
Disclaimer and Educational Use
This script is intended for educational and informational purposes. It does not constitute financial advice, nor does it guarantee trading success. Sudden economic events, low-liquidity times, and unexpected market behaviors can all undermine technical signals. Traders should use proper testing procedures (backtesting and forward-testing) and maintain disciplined risk management measures.
Tradespot - VolatilityTradespot Volatility
A simple, effective representation of volatility within a given asset.
Works out of the box, no configuration required
Allows a trader, especially on leverage, to identify areas of high/low volatility that might influence their exit and entry strategy
Outlier spikes or dips in volatility
Visual representation of a market that is accumulating or a market going parabolic which can help influence your exit and entry parameters
Want to know more or have questions? Come join the Tradespot community, or send me a message on TradingView.
Volatility Signaling 50SMAOverview of the Script:
The script implements a volatility signaling indicator using a 50-period Simple Moving Average (SMA). It incorporates Bollinger Bands and the Average True Range (ATR) to dynamically adjust the SMA's color based on volatility conditions. Here's a detailed breakdown:
Components of the Script:
1. Inputs:
The script allows the user to customize key parameters for flexibility:
Bollinger Bands Length (length): Determines the period for calculating the Bollinger Bands.
Source (src): The price data to use, defaulting to the closing price.
Standard Deviation Multiplier (mult): Scales the Bollinger Bands' width.
ATR Length (atrLength): Sets the period for calculating the ATR.
The 50-period SMA length (smaLength) is fixed at 50.
2. Bollinger Bands Calculation:
Basis: Calculated as the SMA of the selected price source over the specified length.
Upper and Lower Bands: Determined by adding/subtracting a scaled standard deviation (dev) from the basis.
3. ATR Calculation:
Computes the Average True Range over the user-defined atrLength.
4. Volatility-Based Conditions:
The script establishes thresholds for Bollinger Band width relative to ATR:
Yellow Condition: When the band width (upper - lower) is less than 1.25 times the ATR.
Orange Condition: When the band width is less than 1.5 times the ATR.
Red Condition: When the band width is less than 1.75 times the ATR.
5. Dynamic SMA Coloring:
The 50-period SMA is colored based on the above conditions:
Yellow: Indicates relatively low volatility.
Orange: Indicates moderate volatility.
Red: Indicates higher volatility.
White: Default color when no conditions are met.
6. Plotting the 50-Period SMA:
The script plots the SMA (sma50) with a dynamically assigned color, enabling visual analysis of market conditions.
Use Case:
This script is ideal for traders seeking to assess market volatility and identify changes using Bollinger Bands and ATR. The colored SMA provides an intuitive way to gauge market dynamics directly on the chart.
Example Visualization:
Yellow SMA: The market is in a low-volatility phase.
Orange SMA: Volatility is picking up but remains moderate.
Red SMA: Higher volatility, potentially signaling significant market activity.
White SMA: Neutral/default state.
T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering [Loxx]T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering is a VQI indicator that uses T3 smoothing and discontinued signal lines to determine breakouts and breakdowns. This also allows filtering by pips.***
What is the Volatility Quality Index ( VQI )?
The idea behind the volatility quality index is to point out the difference between bad and good volatility in order to identify better trade opportunities in the market. This forex indicator works using the True Range algorithm in combination with the open, close, high and low prices.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included
Signals
Alerts
Related indicators
Zero-line Volatility Quality Index (VQI)
Volatility Quality Index w/ Pips Filtering
Variety Moving Average Waddah Attar Explosion (WAE)
***This indicator is tuned to Forex. If you want to make it useful for other tickers, you must change the pip filtering value to match the asset. This means that for BTC, for example, you likely need to use a value of 10,000 or more for pips filter.
Relative Volume Historical Volatility MCMRelative Volume Historical Volatility
Volume Historical Volatility is relative to it's doubled lookback period of the volume historical volatility to calculate relative volume historical volatility .
Including a standard deviation to calculate the volume volatility value itself is useless. It filters out 32% of the most volatile volume movements of the asset that you are observing.
Example of RVHV:
Period of Volume Volatility Value (POVVV) : 10
Relative Volume Historical Volatility : POVVV / POVVV*2
Historical Volume Volatility of past 10 Bars is compared to the historical volatility of the bast 20 bars to show real growth/decrease of volatility relative to the time of the performing asset.
Comparing historical volume volatility to the current bar includes much more noise, the relative volume historical volatility can be perceived as a smoothed historical volatility ind.
Marginal notes:
Added standard deviations adjusted to the relative volume volatility value to predict probable future volatility of the stock.
