(JS)S&P 500 Volatility Oscillator For Options 2.0I am going to start taking requests to open source my indicators and they will also be updated to Version 4 of Pinescript.
I added some features to the original code such the ability to smooth the oscillator and select the look back periods for the historical volatility.
Link to original:
Original post:
"The idea for this started here: www.tradingview.com with the user @dime
This should only be used on SPX or SPY (though you could use it on other things for correlation I suppose) given that the instrument used to create this calculation is derived from the S&P 500 (thank you VIX ). There's a lot of moving parts here though, so allow me to explain...
First: The main signal is when Implied Volatility (from VIX ) drops beneath Historical Volatility - which is what you want to see so you aren't purchasing a ton of premium on long options. Green and above 0 means that IV% has dropped lower than Historical Volatility . (this signal, for example, would suggest using a Long Call or Put depending on your sentiment)
Second: The green line running underneath zero is the bottom portion of the "Average True Range" derived from the values used to create the oscillator. the closer the bottom histogram is to the green line, the more "normal" IV% is. Obviously, if this gets far away from the line then it could be setting up nicely to short options and sell the IV premium to someone else. (this signal, for example, would suggest using something like a Bull Put Spread)
Third: The red background along with the white line that drops down below zero signals when (and how far) the IV% from 3 months out (from VIX3M ) is less than the current IV%. This would signal the current environment has IV way too high, a signal to short options once again (and don't take any long option positions!).
Tried to make this simple, yet effective. If you trade options on SPX , SPY , even ES1! futures - this is a tool tailored specifically for you! As I said before, if you want you can use it for correlation on other securities. Any other ideas or suggestions surrounding this, please let me know! Enjoy!
Feb 17, 2019
Release Notes: Cosmetic update for a much cleaner look:
-Replaced the "HIGH IV" with a simlple "H"
-Now the white line is constantly showing you the relationship between VIX and VIX3M - when VIX is greater than VIX3M the background still goes red
-However, now when VIX drops below Historical Volatility, the background is bright green
-When both above are true - it's dark green
-The Average True Range on the bottom is now a series of crosses"
Cerca negli script per "Volatility"
Volume Volatility SpectrumThis indicator estimates price volatility and it is based on Volume only (presumably Tick Volume in Forex).
Tick volume is supposed to be a good proxy to actual volume in spot forex (study of Caspar Marney, 2011)
The advantage of this indicator is that it can be used with any pair, any timeframe.
The only parameters are the periods of the reference Volume Moving Average and the fast Volume MA.
The fluctuations of a short period Volume MA with respect to a gently MA with high period
are calculated.
RED areas depict low volatility
GREEN areas depict high volatility.
When the clouds are outside the region delimited by the aqua lines we have extreme conditions:
Extremely low volatility = red cloud outside the aqua bands
Extremely high volatility = green cloud outside the aqua bands
Vitelot/yanez/Vts September 2019.
Compare this indicator with the ATR Volatility Spectrum of myself
ATR Volatility Spectrum
This indicator estimates price volatility and it is based on ATR only.
The advantage of this indicator is that it can be used with any pair, any time frame.
The fluctuations of a short period ATR with respect to a gently ATR with high period
are calculated.
The only parameters are the periods of the reference ATR and fast ATR, which could be
safely let untouched and modified by experts.
RED areas depict low volatility
GREEN areas depict high volatility.
When the clouds are outside the region delimited by the aqua lines we have
extreme conditions:
Extremely low volatility = red cloud outside the aqua bands
Extremely high volatility = green cloud outside the aqua bands
Vitelot/yanez/Vts December 2018.
Hitting the like button is free act of gratitude
Ultimate Volatility Scanner by NHBprod - Requested by Client!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto and stock trading! This strategy is for BITCOIN but can be used on any stock or crypto. This was requested by a client so I thought I should create it and hopefully build off of it and build variants!
This script gets and compares the 14-day volatility using the ATR percentage for a list of cryptocurrencies and stocks. Cryptocurrencies are preloaded into the script, and the script will show you the TOP 5 coins in terms of volatility, and then compares it to the Bitcoin volatility as a reference. It updates these values once per day using daily timeframe data from TradingView. The coins are then sorted in descending order by their volatility.
If you don't want to use the preloaded set of coins, you have the option of inputting your own coins AND/OR stocks!
Let me know your thoughts.
Multiple Frequency Volatility CorrelationThis is a complex indicator that looks to provide some insight into the correlation between volume and price volatility.
Rising volatility is depicted with the color green while falling volatility is depicted with purple.
Lightness of the color is used to depict the length of the window used, darker == shorter in the 2 -> 512 window range.
Inverse MACD + DMI Scalping with Volatility Stop (By Coinrule)This script is focused on shorting during downtrends and utilises two strength based indicators to provide confluence that the start of a short-term downtrend has occurred - catching the opportunity as soon as possible.
This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to hodl this cash, or use it to reinvest when the market reaches attractive buying levels.
Alternatively, you can use this when trading contracts on futures markets where there is no need to already own the underlying asset prior to shorting it.
ENTRY
The trading system uses the Momentum Average Convergence Divergence (MACD) indicator and the Directional Movement Index (DMI) indicator to confirm when the best time is for selling. Combining these two indicators prevents trading during uptrends and reduces the likelihood of getting stuck in a market with low volatility.
The MACD is a trend following momentum indicator and provides identification of short-term trend direction. In this variation it utilises the 12-period as the fast and 26-period as the slow length EMAs, with signal smoothing set at 9.
The DMI indicates what way price is trending and compares prior lows and highs with two lines drawn between each - the positive directional movement line (+DI) and the negative directional movement line (-DI). The trend can be interpreted by comparing the two lines and what line is greater. When the negative DMI is greater than the positive DMI, there are more chances that the asset is trading in a sustained downtrend, and vice versa.
The system will enter trades when two conditions are met:
1) The MACD histogram turns bearish.
2) When the negative DMI is greater than the positive DMI.
EXIT
The strategy comes with a fixed take profit combined with a volatility stop, which acts as a trailing stop to adapt to the trend's strength. Depending on your long-term confidence in the asset, you can edit the fixed take profit to be more conservative or aggressive.
The position is closed when:
Take-Profit Exit: +8% price decrease from entry price.
OR
Stop-Loss Exit: Price crosses above the volatility stop.
In general, this approach suits medium to long term strategies. The backtesting for this strategy begins on 1 April 2022 to 18 July 2022 in order to demonstrate its results in a bear market. Back testing it further from the beginning of 2022 onwards further also produces good returns.
Pairs that produce very strong results include SOLUSDT on the 45m timeframe, MATICUSDT on the 2h timeframe, and AVAUSDT on the 1h timeframe. Generally, the back testing suggests that it works best on the 45m/1h timeframe across most pairs.
A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Combo Backtest 123 Reversal & Statistical Volatility This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
This indicator used to calculate the statistical volatility, sometime
called historical volatility, based on the Extreme Value Method.
Please use this link to get more information about Volatility.
WARNING:
- For purpose educate only
- This script to change bars colors.
Statistical Volatility - Extreme Value Method This indicator used to calculate the statistical volatility, sometime
called historical volatility, based on the Extreme Value Method.
Please use this link to get more information about Volatility.
Relative Volatility Index The RVI is a modified form of the relative strength index (RSI).
The original RSI calculation separates one-day net changes into
positive closes and negative closes, then smoothes the data and
normalizes the ratio on a scale of zero to 100 as the basis for the
formula. The RVI uses the same basic formula but substitutes the
10-day standard deviation of the closing prices for either the up
close or the down close. The goal is to create an indicator that
measures the general direction of volatility. The volatility is
being measured by the 10-days standard deviation of the closing prices.
Fibonacci Ratios with VolatilityThis script will plot Fibonacci ratios with volatility. The Fibonacci retracement and extensions are plotted in lower time frames up to 15 minutes and therefore, it can be used for intraday only.
Odin's Volume and Volatility CompositeA simple indicator showing the ratio between Historical Volume and Historical Volatility.
It's meant to be applied to the BitMEX XBTUSD chart.
You can use this to develop profitable breakout strategies.
Historical Volatility based Standard Deviation_V2This Plots the Standard Deviation Price Band based on the Historical Volatility. SD 1, 2, 3.
Version update:
Fixed the Standard Deviation mistake on Version 1.
Added Smoothing Options for those who prefer a less choppy version.
Standard Deviation 3 plot is not set to Default
FVE Volatility color-coded Volume bar The FVE is a pure volume indicator. Unlike most of the other indicators
(except OBV), price change doesn?t come into the equation for the FVE
(price is not multiplied by volume), but is only used to determine whether
money is flowing in or out of the stock. This is contrary to the current trend
in the design of modern money flow indicators. The author decided against a
price-volume indicator for the following reasons:
- A pure volume indicator has more power to contradict.
- The number of buyers or sellers (which is assessed by volume) will be the same,
regardless of the price fluctuation.
- Price-volume indicators tend to spike excessively at breakouts or breakdowns.
This study is an addition to FVE indicator. Indicator plots different-coloured volume
bars depending on volatility.
Hawkes Volatility Exit IndicatorOverview
The Hawkes Volatility Exit Indicator is a powerful tool designed to help traders capitalize on volatility breakouts and exit positions when momentum fades. Built on the Hawkes process, it models volatility clustering to identify optimal entry points after quiet periods and exit signals during volatility cooling. Designed to be helpful for swing traders and trend followers across markets like stocks, forex, and crypto.
Key Features Volatility-Based Entries: Detects breakouts when volatility spikes above the 95th percentile (adjustable) after quiet periods (below 5th percentile).
This indicator is probably better on exits than entries.
Smart Exit Signals: Triggers exits when volatility drops below a customizable threshold (default: 30th percentile) after a minimum hold period.
Hawkes Process: Uses a decay-based model (kappa) to capture volatility clustering, making it responsive to market dynamics.
Visual Clarity: Includes a volatility line, exit threshold, percentile bands, and intuitive markers (triangles for entries, X for exits).
Status Table: Displays real-time data on position (LONG/SHORT/FLAT), volatility regime (HIGH/LOW/NORMAL), bars held, and exit readiness.
Customizable Alerts: Set alerts for breakouts and exits to stay on top of trading opportunities.
How It Works Quiet Periods: Identifies low volatility (below 5th percentile) that often precede significant moves.
Breakout Entries: Signals bullish (triangle up) or bearish (triangle down) entries when volatility spikes post-quiet period.
Exit Signals: Suggests exiting when volatility cools below the exit threshold after a minimum hold (default: 3 bars).
Visuals & Table: Tracks volatility, position status, and signals via lines, shaded zones, and a detailed status table.
Settings
Hawkes Kappa (0.1): Adjusts volatility decay (lower = smoother, higher = more sensitive).
Volatility Lookback (168): Sets the period for percentile calculations.
ATR Periods (14): Normalizes volatility using Average True Range.
Breakout Threshold (95%): Volatility percentile for entries.
Exit Threshold (30%): Volatility percentile for exits.
Quiet Threshold (5%): Defines quiet periods.
Minimum Hold Bars (3): Ensures positions are held before exiting.
Alerts: Enable/disable breakout and exit alerts.
How to Use
Entries: Look for triangle markers (up for long, down for short) and confirm with the status table showing "ENTRY" and "LONG"/"SHORT."
Exits: Exit on X cross markers when the status table shows "EXIT" and "Exit Ready: YES."
Monitoring: Use the status table to track position, bars held, and volatility regime (HIGH/LOW/NORMAL).
Combine: Pair with price action, support/resistance, or other indicators for better context.
Tips : Adjust thresholds for your market: lower breakout thresholds for more signals, higher exit thresholds for earlier exits.
Test on your asset to ensure compatibility (best for markets with volatility clustering).
Use alerts to automate signal detection.
Limitations Requires sufficient data (default: 168 bars) for reliable signals. Check "Data Status" in the table.
Focuses on volatility, not price direction—combine with trend tools.
May lag slightly due to the smoothing nature of the Hawkes process.
Why Use It?
The Hawkes Volatility Exit Indicator offers a unique, data-driven approach to timing trades based on volatility dynamics. Its clear visuals, customizable settings, and real-time status table make it a valuable addition to any trader’s toolkit. Try it to catch breakouts and exit with precision!
This indicator is based on neurotrader888's python repo. All credit to him. All mistakes mine.
This conversion published for wider attention to the Hawkes method.
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
EMA Volatility Channel [QuantAlgo]EMA Volatility Channel 🌊📈
The EMA Volatility Channel by QuantAlgo is an advanced technical indicator designed to capture price volatility and trend dynamics through adaptive channels based on exponential moving averages. This sophisticated system combines EMA-based trend analysis with dynamic volatility-adjusted bands to help traders and investors identify trend direction, potential reversals, and market volatility conditions. By evaluating both price momentum and volatility together, this tool enables users to make informed trading decisions while adapting to changing market conditions.
💫 Dynamic Channel Architecture
The EMA Volatility Channel provides a unique framework for assessing market trends through a blend of exponential moving averages and volatility-based channel calculations. Unlike traditional channel indicators that use fixed-width bands, this system incorporates dynamic volatility measurements to adjust channel width automatically, helping users determine whether price movements are significant relative to current market conditions. By combining smooth EMA trends with adaptive volatility bands, it evaluates both directional movement and market volatility, while the smoothing parameters ensure stable yet responsive channel adjustments. This adaptive approach allows users to identify trending conditions while remaining aware of volatility expansions and contractions, enhancing both trend-following and reversal strategies.
📊 Indicator Components & Mechanics
The EMA Volatility Channel is composed of several technical components that create a dynamic channel system:
EMA Midline: Calculates a smoothed exponential moving average that serves as the channel's centerline, providing a clear reference for trend direction.
Volatility Measurement: Computes average price movement to determine dynamic channel width, adapting to changing market conditions automatically.
Smooth Band Calculation: Applies additional smoothing to the channel bands, reducing noise while maintaining responsiveness to significant price movements.
📈 Key Indicators and Features
The EMA Volatility Channel combines various technical tools to deliver a comprehensive analysis of market conditions.
The indicator utilizes exponential moving averages with customizable length and smoothing parameters to adapt to different trading styles. Volatility calculations are applied to determine channel width, providing context-aware boundaries for price movement. The trend detection component evaluates price action relative to the channel bands, helping validate trends and identify potential reversals.
The indicator incorporates multi-layered visualization with color-coded channels and bars to signal both trend direction and market position. These adaptive visual cues, combined with programmable alerts for channel breakouts, help traders and investors track both trend changes and volatility conditions, supporting both trend-following and mean-reversion strategies.
⚡️ Practical Applications and Examples
✅ Add the Indicator: Add the indicator to your TradingView chart by clicking on the star icon to add it to your favorites ⭐️
👀 Monitor Channel Position: Watch the price position relative to the channel bands to identify trend direction and potential reversals. When price moves outside the channel, consider potential trend changes or extreme conditions.
🔔 Set Alerts: Configure alerts for channel breakouts and trend changes, ensuring you can act on significant technical developments promptly.
🌟 Summary and Tips
The EMA Volatility Channel by QuantAlgo is a versatile technical tool, designed to support both trend following and volatility analysis across different market environments. By combining smooth EMA trends with dynamic volatility-based channels, it helps traders and investors identify significant price movements while measuring market volatility, providing reliable technical signals. The tool's adaptability across timeframes makes it suitable for both trend-following and reversal strategies, allowing users to capture opportunities while maintaining awareness of changing market conditions.
Low Volatility Range Breaks [BigBeluga]Low Volatility Range Breaks
The Low Volatility Range Breaks indicator is an advanced technical analysis tool designed to identify periods of low volatility and potential breakout opportunities. By visualizing low volatility ranges as ranges and tracking subsequent price movements, this indicator helps traders spot potential high-probability trade setups.
🔵 KEY FEATURES
● Low Volatility Detection
Identifies periods of low volatility based on highest and lowest periods and user-defined sensitivity
Uses a combination of highest/lowest price calculations and ATR for dynamic adaptation
● Volatility Box Visualization
Creates a box to represent the low volatility range
Box height is adjustable based on ATR multiplier
Includes a mid-line for reference within the box
● Breakout Detection
Identifies when price breaks above or below the volatility box
Labels breakouts as "Break Up" or "Break Dn" on the chart
Changes box appearance to indicate a completed breakout
● Probability Tracking
Counts the number of closes above and below the box's mid-line
Displays probability counters for potential upward and downward moves
Resets counters after a confirmed breakout
🔵 HOW TO USE
● Identifying Low Volatility Periods
Watch for the formation of volatility boxes on the chart
These boxes represent periods where price movement has been confined
● Anticipating Breakouts
Monitor price action as it approaches the edges of the volatility box
Use the probability counters to gauge the likely direction of the breakout
● Trading Breakouts
Consider posible entering trades when price breaks above or below the volatility box
Use the breakout labels ("Break Up" or "Break Dn") as a trading opportunity
● Managing Risk
Use the opposite side of the volatility box as a potential invalidation level
Consider the box height for position sizing and risk management
● Trend Analysis
Multiple upward breakouts may indicate a developing uptrend
Multiple downward breakouts may suggest a forming downtrend
Use in conjunction with other trend indicators for confirmation
🔵 CUSTOMIZATION
The Low Volatility Box Breaks indicator offers several customization options:
Adjust the volatility length to change the period for highest/lowest price calculations
Modify the volatility level to fine-tune the sensitivity of low volatility detection
Adjust the box height multiplier to change the size of volatility boxes
By fine-tuning these settings, traders can adapt the indicator to various market conditions and personal trading strategies.
The Low Volatility Range Breaks indicator provides a unique approach to identifying potential breakout opportunities following periods of consolidation. By visually representing low volatility periods and tracking subsequent price movements, it offers traders a powerful tool for spotting high-probability trade setups.
This indicator can be particularly useful for traders focusing on breakout strategies, mean reversion tactics, or those looking to enter trades at the beginning of new trends. The combination of visual cues (boxes and breakout labels) and quantitative data (probability counters) provides a comprehensive view of market dynamics during and after low volatility periods.
As with all technical indicators, it's recommended to use the Low Volatility Range Breaks indicator in conjunction with other forms of analysis and within the context of a well-defined trading strategy. While this indicator can provide valuable insights into potential breakouts, it should be considered alongside other factors such as overall market trends, volume, and fundamental analysis when making trading decisions.
Low Volatility Breakout in Trend
█ OVERVIEW
"Low Volatility Breakout in Trend" is a technical analysis tool that identifies periods of low-volatility consolidation within an ongoing trend and signals potential breakouts aligned with the trend's direction. The indicator detects trends using a simple moving average (SMA) of price, identifies consolidation zones based on the size of candle bodies, and displays the percentage change in volume (volume delta) at the breakout moment.
█ CONCEPTS
The core idea of the indicator is to pinpoint moments where traders can join an ongoing trend by capitalizing on breakouts from consolidation zones, supported by additional information such as volume delta. It provides clear visualizations of trends, consolidation zones, and breakout signals to facilitate trading decisions.
Why Use It?
* Breakout Identification: The indicator locates low-volatility consolidation zones (measured by the size of individual candle bodies, not the price range of the consolidation) and signals breakouts, enabling traders to join the trend at key moments.
* Volume Analysis: Displays the percentage change in volume (delta) relative to its simple moving average, providing insight into market activity rather than acting as a signal filter.
* Visual Clarity: Colored trend lines, consolidation boxes (drawn only after the breakout candle closes, not on subsequent candles), and volume delta labels enable quick chart analysis.
* Flexibility: Adjustable parameters, such as the volatility window length or SMA period, allow customization for various trading strategies and markets.
How It Works
* Trend Detection: The indicator calculates a simple moving average (SMA) of price (default: based on the midpoint of high/low) and creates dynamic trend bands, offset by a percentage of the average candle height (band scaling). A price above the upper band signals an uptrend, while a price below the lower band indicates a downtrend. Trend changes occur not when the price crosses the SMA but when it crosses above the upper band or below the lower band (offset by the average candle height multiplied by the scaling factor).
* Consolidation Identification: Identifies low-volatility zones when the candle body size is smaller than the average body size over a specified period (default: 20 candles) multiplied by a volatility threshold — the maximum allowable body size as a percentage of the average body (e.g., 2 means the candle body must be less than twice the average body to be considered low-volatility).
* Breakout Signals: A breakout occurs when the candle body exceeds the volatility threshold, is larger than the maximum body in the consolidation, and aligns with the trend direction (bullish in an uptrend, bearish in a downtrend).
* Visualization: Draws a trend line with a gradient, consolidation boxes (appearing only after the breakout candle closes, marking the consolidation zone), and volume delta labels. Optionally displays breakout signal arrows.
* Signals and Alerts: The indicator generates signals for bullish and bearish breakouts, including the volume delta percentage. Alerts are an additional feature that can be enabled for notifications.
Settings and Customization
* Volatility Window: Length of the period for calculating the average candle body size (default: 20).
* Volatility Threshold: Maximum candle body size as a percentage of the average body (default: 2).
* Minimum Consolidation Bars: Number of candles required for a consolidation (default: 10).
* SMA Length for Trend: Period of the SMA for trend detection (default: 100).
* Band Scaling: Offset of trend bands as a percentage of the average candle height (default: 250%), determining the distance from the SMA.
* Visualization Options: Enable/disable consolidation boxes (Show Consolidation Boxes, drawn after the breakout candle closes), volume delta labels (Show Volume Delta Labels), and breakout signals (Show Breakout Signals, e.g., triangles).
* Colors: Customize colors for the trend line, consolidation boxes, and volume delta labels.
█ OTHER SECTIONS
Usage Examples
* Joining an Uptrend: When the price breaks out of a consolidation in an uptrend with a volume delta of +50%, open a long position; the signal is stronger if the breakout candle surpasses a local high.
* Avoiding False Breakouts: Ignore breakout signals with low volume delta (e.g., below 0%) and combine the indicator with other tools (e.g., support/resistance levels or oscillators) to confirm moves in low-activity zones.
Notes for Users
* On markets that do not provide volume data, the indicator will not display volume delta — disable volume labels and enable breakout signals (e.g., triangles) instead.
* Adjust parameters to suit the market's characteristics to minimize noise.
* Combine with other tools, such as Fibonacci levels or oscillators, for greater precision.
Disparity Index with Volatility ZonesDisparity Index with Volatility Zones
is a momentum oscillator that measures the percentage difference between the current price and its simple moving average (SMA). This allows traders to identify overbought/oversold conditions, assess momentum strength, and detect potential trend reversals or continuations.
🔍 Core Concept:
The Disparity Index (DI) is calculated as:
DI = 100 × (Price − SMA) / SMA
A positive DI indicates the price is trading above its moving average (potential bullish sentiment), while a negative DI suggests the price is below the average (potential bearish sentiment).
This version of the Disparity Index introduces a dual-zone volatility framework, offering deeper insight into the market's current state.
🧠 What Makes This Version Unique?
1. High Volatility Zones
When DI crosses above +1.0% or below –1.0%, it often indicates the start or continuation of a strong trend.
Sustained readings beyond these thresholds typically align with trending phases, offering opportunities for momentum-based entries.
A reversal back within ±1.0% after exceeding these levels can suggest a shift in momentum — similar to how RSI exits the overbought/oversold zones before reversals.
These thresholds act as dynamic markers for breakout confirmation and potential trend exhaustion.
2. Low Volatility Zones
DI values between –0.5% and +0.5% define the low-volatility zone, shaded for visual clarity.
This area typically indicates market indecision, sideways price action, or consolidation.
Trading within this range may favor range-bound or mean-reversion strategies, as trend momentum is likely limited.
The logic is similar to interpreting a flat ADX, tight Bollinger Bands, or contracting Keltner Channels — all suggesting consolidation.
⚙️ Features:
Customizable moving average length and input source
Adjustable thresholds for overbought/oversold and low-volatility zones
Optional visual fill between low-volatility bounds
Clean and minimal chart footprint (non-essential plots hidden by default)
📈 How to Use:
1. Trend Confirmation:
A break above +1.0% can be used as a bullish continuation signal.
A break below –1.0% may confirm bearish strength.
Long periods above/below these thresholds support trend-following entries.
2. Reversal Detection:
If DI returns below +1.0% after exceeding it, bullish momentum may be fading.
If DI rises above –1.0% after falling below, bearish pressure may be weakening.
These shifts resemble overbought/oversold transitions in oscillators like RSI or Stochastic, and can be paired with divergence, volume, or price structure analysis for higher reliability.
3. Sideways Market Detection:
DI values within ±0.5% indicate low volatility or a non-trending environment.
Traders may avoid breakout entries during these periods or apply range-trading tactics instead.
Observing transitions out of the low-volatility zone can help anticipate breakouts.
4. Combine with Other Indicators:
DI signals can be enhanced using tools like MACD, Volume Oscillators, or Moving Averages.
For example, a DI breakout beyond ±1.0% supported by a MACD crossover or volume spike can help validate trend initiation.
This indicator is especially powerful when paired with Bollinger Bands:
A simultaneous price breakout from the Bollinger Band and DI moving beyond ±1.0% can help identify early trend inflection points.
This combination supports entering positions early in a developing trend, improving the efficiency of trend-following strategies and enhancing decision-making precision.
It also helps filter false breakouts when DI fails to confirm the move outside the band.
This indicator is designed for educational and analytical purposes and works across all timeframes and asset classes.
It is particularly useful for traders seeking a clear framework to identify momentum strength, filter sideways markets, and improve entry timing within a larger trading system.
Garman-Klass-Yang-Zhang Volatility EstimatorThe Garman-Klass-Yang-Zhang Volatility Estimator (GKYZVE) is yet another attempt to robustly measure volatility, integrating intra-candle and inter-candle dynamics. It is an extension of the Garman-Klass Volatility Estimator (GKVE) incorporating insights from the Yang-Zhang Volatility Estimator (YZVE) . Like the YZVE, the GKYZVE holistically considers open, high, low, and close prices. The formula for GKYZ is:
GKYZVE = 0.5 * σ_HL² + * σ_CC² + σ_OC²
Where:
σ_HL² is the variance based on the high and low prices (σ_HL² = (high - low)² / (4 * math.log(2))), weighted at 0.5.
σ_CC² is the close-to-close variance (σ_CC² = (close - close)²), weighted at (2 ln 2) -1 for the logarithmic distribution of returns and emphasizing the impact of day-to-day price changes.
σ_OC² is the variance of the opening price against the closing price (σ_OC² = 0.5 * (open - close)²), weighted at 1.
The GKYZVE differs from the YZVE by using fixed weighing factors derived from theoretical calculations, leaning heavier into the assumption that returns are log-distributed.
This script also offers a choice for normalization between 0 and 1, turning the estimator into an oscillator for comparing current volatility to recent levels. Horizontal lines at user-defined levels are also available for clearer visualization. Both options are off by default.
References:
Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67-78.
Yang, D., & Zhang, Q. (2000). Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3), 477-492.
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
LBR-Volatility Breakout BarsThe originator of this script is Linda Raschke of LBR Group.
This Pine Script code is the version 5 of LBR Paintbars for TradingView, called "LBR-Bars." It was originally coded for TradingView in version 3 by LazyBear. It is a complex indicator that combines various features such as coloring bars based on different conditions, displaying Keltner channels, and showing volatility lines.
Let me break down the key components and explain how it works:
1. Inputs Section: This section defines various input parameters that users can adjust when adding the indicator to their charts. These parameters allow users to customize the behavior and appearance of the indicator. Here are some of the key input parameters:
- Users can control whether to color bars under different conditions. For example,
they can choose to color LBR bars, color bars above/below Kelts, or color non-LBR
bars.
- Users can choose whether to show volatility lines or shade Keltner channels' area
with the Mid being the moving average on the chart.
- In the calculation of Keltner channels, users can set the length of the moving
average that the Keltner channels use as the mid and then set the Keltner multiplier.
If users want to use "True Range" to determine calculations, they can turn it on or
off; it defaults to off.
- Users can change the calculation of volatility lines and set the length for finding the
lowest and highest prices. The user sets the ATR length and multiplier for the ATR.
2. Calculation Section: This section defines the calculation of the upper and lower standard deviation bands based on the input parameters. It uses Exponential Moving Averages (EMAs) and optionally True Range to calculate these bands if turned on. These bands are used in the Keltner channel calculation.
3. Keltner Channel Section: This section calculates the upper, middle, and lower lines of the Keltner channels. It also plots these lines on the chart. The colors and visibility of these lines are controlled by user inputs.
4. Volatility Lines Section: This section calculates the upper and lower volatility lines based on the lowest and highest prices over a specified period and the ATR. It also checks whether the current close price is above or below these lines accordingly. The colors and visibility of these lines are controlled by user inputs.
5. Bar Colors Section: This section determines the color of the bars on the chart based on various conditions. It checks whether the current bar meets conditions like being an LBR bar, being above or below volatility lines, or being in "No Man's Land." The color of the bars is set accordingly based on user inputs.
This Pine Script creates an indicator that provides visual cues on the chart based on Keltner channels, volatility lines, and other customizable conditions. Users can adjust the input parameters to tailor the indicator's behavior and appearance to their trading preferences.
Normal Distribution Asymmetry & Volatility ZonesNormal Distribution Asymmetry & Volatility Zones Indicator provides insights into the skewness of a price distribution and identifies potential volatility zones in the market. The indicator calculates the skewness coefficient, indicating the asymmetry of the price distribution, and combines it with a measure of volatility to define buy and sell zones.
The key features of this indicator include :
Skewness Calculation : It calculates the skewness coefficient, a statistical measure that reveals whether the price distribution is skewed to the left (negative skewness) or right (positive skewness).
Volatility Zones : Based on the skewness and a user-defined volatility threshold, the indicator identifies buy and sell zones where potential price movements may occur. Buy zones are marked when skewness is negative and prices are below a volatility threshold. Sell zones are marked when skewness is positive and prices are above the threshold.
Signal Source Selection : Traders can select the source of price data for analysis, allowing flexibility in their trading strategy.
Customizable Parameters : Users can adjust the length of the distribution, the volatility threshold, and other parameters to tailor the indicator to their specific trading preferences and market conditions.
Visual Signals : Buy and sell zones are visually displayed on the chart, making it easy to identify potential trade opportunities.
Background Color : The indicator changes the background color of the chart to highlight significant zones, providing a clear visual cue for traders.
By combining skewness analysis and volatility thresholds, this indicator offers traders a unique perspective on potential market movements, helping them make informed trading decisions. Please note that trading involves risks, and this indicator should be used in conjunction with other analysis and risk management techniques.
