Volatility SkewThis indicator measure the historical skew of actual volatility for an individual security. It measure the volatility of up moves versus down moves over the period and gives a ratio. When the indicator is greater than one, it indicators that volatility is greater to the upside, when it is below 1 it indicates that volatility is skewed to the downside.
This is not comparable to the SKEW index, since that measures the implied volatility across option strikes, rather than using historical volatility.
Search in scripts for "Volatility"
Volatility RegimeThis is a useful volatility indicator to be used on a Daily time Frame
and paired with the Market Regime indicator.
Gray means that the markets have low volatility (calm) and might be consolidating before resuming or reversing the trend.
Teal means that volatility is somewhere around the average.
And Yellow means it has spiked up and we are in a high volatility regime.
When you pair it with the Market Regime filter for Bull/Bear markets you will
find that the best bull markets start from low volatility and mature on high volatility.
More often than not, the top comes when The Market Regime is very strong (Super Bull)
and volatility is high.
Similarly, the bottom arrives when the Market Regime is very bearish (Super Bear)
and volatility is high.
Volatility % Bands (O→C)Volatility % Bands (O→C) is an indicator designed to visualize the percentage change from Open to Close of each candle, providing a clear view of short-term momentum and volatility.
**Histogram**: Displays bar-by-bar % change (Close vs Open). Green bars indicate positive changes, while red bars indicate negative ones, making momentum shifts easy to identify.
**Moving Average Line**: Plots the Simple Moving Average (SMA) of the absolute % change, helping traders track the average volatility over a chosen period.
**Background Bands**: Based on the user-defined Level Step, ±1 to ±5 zones are highlighted as shaded bands, allowing quick recognition of whether volatility is low, moderate, or extreme.
**Label**: Shows the latest candle’s % change and the current SMA value as a floating label on the right, making it convenient for real-time monitoring.
This tool can be useful for volatility breakout strategies, day trading, and short-term momentum analysis.
Volatility Index of Range Verification█ OVERVIEW
This is a volatility indicator created by extending concepts from Tushar Chande's Range Action Verification Index (RAVI).
█ CONCEPTS
This indicator constructs range of the RAVI indicator. It uses this range to build a histogram that represents how fast the range is changing, or a measure of volatility. A line is then constructed, either from a moving average or standard deviation depending on the settings that can serve as an action trigger.
█ INPUTS
• Fast MA Period: the period of the quickest moving average that is used to build the RAVI indicator line
• Slow MA Period: the period of the slowest moving average that is used to build the RAVI indicator line
• MA Type: the type of moving average to use, either Simple or Exponential
• Price Source: the type of price source to use; close, high, low, hlc3, etc.
• Lookback Period: how far back to construct the minimum and maximum of the range
• Standard Range: the standard range of the indicator. a smaller range will exaggerate differences in the columns, and vice-versa
• Volatility Period: the period used for the trigger line moving average
• Std. Deviation Mode?: Whether the trigger line will plot using a moving average or a multiple of Standard Deviation.
• Deviation Multiplier: How many deviations to use if the trigger line is in Std. Deviation Mode
Volatility Targeting: Single Asset [BackQuant]Volatility Targeting: Single Asset
An educational example that demonstrates how volatility targeting can scale exposure up or down on one symbol, then applies a simple EMA cross for long or short direction and a higher timeframe style regime filter to gate risk. It builds a synthetic equity curve and compares it to buy and hold and a benchmark.
Important disclaimer
This script is a concept and education example only . It is not a complete trading system and it is not meant for live execution. It does not model many real world constraints, and its equity curve is only a simplified simulation. If you want to trade any idea like this, you need a proper strategy() implementation, realistic execution assumptions, and robust backtesting with out of sample validation.
Single asset vs the full portfolio concept
This indicator is the single asset, long short version of the broader volatility targeted momentum portfolio concept. The original multi asset concept and full portfolio implementation is here:
That portfolio script is about allocating across multiple assets with a portfolio view. This script is intentionally simpler and focuses on one symbol so you can clearly see how volatility targeting behaves, how the scaling interacts with trend direction, and what an equity curve comparison looks like.
What this indicator is trying to demonstrate
Volatility targeting is a risk scaling framework. The core idea is simple:
If realized volatility is low relative to a target, you can scale position size up so the strategy behaves like it has a stable risk budget.
If realized volatility is high relative to a target, you scale down to avoid getting blown around by the market.
Instead of always being 1x long or 1x short, exposure becomes dynamic. This is often used in risk parity style systems, trend following overlays, and volatility controlled products.
This script combines that risk scaling with a simple trend direction model:
Fast and slow EMA cross determines whether the strategy is long or short.
A second, longer EMA cross acts as a regime filter that decides whether the system is ACTIVE or effectively in CASH.
An equity curve is built from the scaled returns so you can visualize how the framework behaves across regimes.
How the logic works step by step
1) Returns and simple momentum
The script uses log returns for the base return stream:
ret = log(price / price )
It also computes a simple momentum value:
mom = price / price - 1
In this version, momentum is mainly informational since the directional signal is the EMA cross. The lookback input is shared with volatility estimation to keep the concept compact.
2) Realized volatility estimation
Realized volatility is estimated as the standard deviation of returns over the lookback window, then annualized:
vol = stdev(ret, lookback) * sqrt(tradingdays)
The Trading Days/Year input controls annualization:
252 is typical for traditional markets.
365 is typical for crypto since it trades daily.
3) Volatility targeting multiplier
Once realized vol is estimated, the script computes a scaling factor that tries to push realized volatility toward the target:
volMult = targetVol / vol
This is then clamped into a reasonable range:
Minimum 0.1 so exposure never goes to zero just because vol spikes.
Maximum 5.0 so exposure is not allowed to lever infinitely during ultra low volatility periods.
This clamp is one of the most important “sanity rails” in any volatility targeted system. Without it, very low volatility regimes can create unrealistic leverage.
4) Scaled return stream
The per bar return used for the equity curve is the raw return multiplied by the volatility multiplier:
sr = ret * volMult
Think of this as the return you would have earned if you scaled exposure to match the volatility budget.
5) Long short direction via EMA cross
Direction is determined by a fast and slow EMA cross on price:
If fast EMA is above slow EMA, direction is long.
If fast EMA is below slow EMA, direction is short.
This produces dir as either +1 or -1. The scaled return stream is then signed by direction:
avgRet = dir * sr
So the strategy return is volatility targeted and directionally flipped depending on trend.
6) Regime filter: ACTIVE vs CASH
A second EMA pair acts as a top level regime filter:
If fast regime EMA is above slow regime EMA, the system is ACTIVE.
If fast regime EMA is below slow regime EMA, the system is considered CASH, meaning it does not compound equity.
This is designed to reduce participation in long bear phases or low quality environments, depending on how you set the regime lengths. By default it is a classic 50 and 200 EMA cross structure.
Important detail, the script applies regime_filter when compounding equity, meaning it uses the prior bar regime state to avoid ambiguous same bar updates.
7) Equity curve construction
The script builds a synthetic equity curve starting from Initial Capital after Start Date . Each bar:
If regime was ACTIVE on the previous bar, equity compounds by (1 + netRet).
If regime was CASH, equity stays flat.
Fees are modeled very simply as a per bar penalty on returns:
netRet = avgRet - (fee_rate * avgRet)
This is not realistic execution modeling, it is just a simple turnover penalty knob to show how friction can reduce compounded performance. Real backtesting should model trade based costs, spreads, funding, and slippage.
Benchmark and buy and hold comparison
The script pulls a benchmark symbol via request.security and builds a buy and hold equity curve starting from the same date and initial capital. The buy and hold curve is based on benchmark price appreciation, not the strategy’s asset price, so you can compare:
Strategy equity on the chart symbol.
Buy and hold equity for the selected benchmark instrument.
By default the benchmark is TVC:SPX, but you can set it to anything, for crypto you might set it to BTC, or a sector index, or a dominance proxy depending on your study.
What it plots
If enabled, the indicator plots:
Strategy Equity as a line, colored by recent direction of equity change, using Positive Equity Color and Negative Equity Color .
Buy and Hold Equity for the chosen benchmark as a line.
Optional labels that tag each curve on the right side of the chart.
This makes it easy to visually see when volatility targeting and regime gating change the shape of the equity curve relative to a simple passive hold.
Metrics table explained
If Show Metrics Table is enabled, a table is built and populated with common performance statistics based on the simulated daily returns of the strategy equity curve after the start date. These include:
Net Profit (%) total return relative to initial capital.
Max DD (%) maximum drawdown computed from equity peaks, stored over time.
Win Rate percent of positive return bars.
Annual Mean Returns (% p/y) mean daily return annualized.
Annual Stdev Returns (% p/y) volatility of daily returns annualized.
Variance of annualized returns.
Sortino Ratio annualized return divided by downside deviation, using negative return stdev.
Sharpe Ratio risk adjusted return using the risk free rate input.
Omega Ratio positive return sum divided by negative return sum.
Gain to Pain total return sum divided by absolute loss sum.
CAGR (% p/y) compounded annual growth rate based on time since start date.
Portfolio Alpha (% p/y) alpha versus benchmark using beta and the benchmark mean.
Portfolio Beta covariance of strategy returns with benchmark returns divided by benchmark variance.
Skewness of Returns actually the script computes a conditional value based on the lower 5 percent tail of returns, so it behaves more like a simple CVaR style tail loss estimate than classic skewness.
Important note, these are calculated from the synthetic equity stream in an indicator context. They are useful for concept exploration, but they are not a substitute for professional backtesting where trade timing, fills, funding, and leverage constraints are accurately represented.
How to interpret the system conceptually
Vol targeting effect
When volatility rises, volMult falls, so the strategy de risks and the equity curve typically becomes smoother. When volatility compresses, volMult rises, so the system takes more exposure and tries to maintain a stable risk budget.
This is why volatility targeting is often used as a “risk equalizer”, it can reduce the “biggest drawdowns happen only because vol expanded” problem, at the cost of potentially under participating in explosive upside if volatility rises during a trend.
Long short directional effect
Because direction is an EMA cross:
In strong trends, the direction stays stable and the scaled return stream compounds in that trend direction.
In choppy ranges, the EMA cross can flip and create whipsaws, which is where fees and regime filtering matter most.
Regime filter effect
The 50 and 200 style filter tries to:
Keep the system active in sustained up regimes.
Reduce exposure during long down regimes or extended weakness.
It will always be late at turning points, by design. It is a slow filter meant to reduce deep participation, not to catch bottoms.
Common applications
This script is mainly for understanding and research, but conceptually, volatility targeting overlays are used for:
Risk budgeting normalize risk so your exposure is not accidentally huge in high vol regimes.
System comparison see how a simple trend model behaves with and without vol scaling.
Parameter exploration test how target volatility, lookback length, and regime lengths change the shape of equity and drawdowns.
Framework building as a reference blueprint before implementing a proper strategy() version with trade based execution logic.
Tuning guidance
Lookback lower values react faster to vol shifts but can create unstable scaling, higher values smooth scaling but react slower to regime changes.
Target volatility higher targets increase exposure and drawdown potential, lower targets reduce exposure and usually lower drawdowns, but can under perform in strong trends.
Signal EMAs tighter EMAs increase trade frequency, wider EMAs reduce churn but react slower.
Regime EMAs slower regime filters reduce false toggles but will miss early trend transitions.
Fees if you crank this up you will see how sensitive higher turnover parameter sets are to friction.
Final note
This is a compact educational demonstration of a volatility targeted, long short single asset framework with a regime gate and a synthetic equity curve. If you want a production ready implementation, the correct next step is to convert this concept into a strategy() script, add realistic execution and cost modeling, test across multiple timeframes and market regimes, and validate out of sample before making any decision based on the results.
Volatility Percentile🎲 Volatility is an important measure to be included in trading plan and strategy. Strategies have varied outcome based on volatility of the instruments in hand.
For example,
🚩 Trend following strategies work better on low volatility instruments and reversal patterns work better in high volatility instruments. It is also important for us to understand the median volatility of an instrument before applying particular strategy strategy on them.
🚩 Different instrument will have different volatility range. For instance crypto currencies have higher volatility whereas major currency pairs have lower volatility with respect to their price. It is also important for us to understand if the current volatility of the instrument is relatively higher or lower based on the historical values.
This indicator is created to study and understand more about volatility of the instruments.
⬜ Process
▶ Volatility metric used here is ATR as percentage of price. Other things such as bollinger bandwidth etc can also be used with few changes.
▶ We use array based counters to count ATR values in different range. For example, if we are measuring ATR range based on precision 2, we will use array containing 10000 values all initially set to 0 which act as 10000 buckets to hold counters of different range. But, based on the ATR percentage range, they will be incremented. Let's say, if atr percent is 2, then 200th element of the array is increased by 1.
▶ When we do this for every bar, we have array of counters which has the division on how many bars had what range of atr percent.
▶ Using this array, we can calculate how many bars had atr percent more than current value, how many had less than current value, and how many bars in history has same atr percent as current value.
▶ With these information, we can calculate the percentile of atr percentage value. We can also plot a detailed table mentioning what percentile each range map to.
⬜ Settings
▶ ATR Parameters - this include Moving average type and Length for atr calculation.
▶ Rounding type refers to rounding ATR percentage value before we put into certain bucket. For example, if ATR percentage 2.7, round or ceil will make it 3, whereas floor will make it 2 which may fall into different buckets based on the precision selected.
▶ Precision refers to how much detailed the range should be. If precision set to 0, then we get array of 100 to collect the range where each value will represent a range of 1%. Similarly precision of 1 will lead to array of 1000 with each item representing range of 0.1. Default value used is 2 which is also the max precision possible in this script. This means, we use array of 10000 to track the range and percentile of the ATR.
▶ Display Settings - Inverse when applied track percentile with respect to lowest value of ATR instead of high. By default this is set to false. Other two options allow users to enable stats table. When detailed stats are enabled, ATR Percentile as plot is hidden.
▶ Table Settings - Allows users to select set size and coloring options.
▶ Indicator Time Window - Allow users to select particular timeframe instead of all available bars to run the study. By default windows are disabled. Users can chose start and end time individually.
Indicator display components can be described as below:
K's Volatility BandsVolatility bands come in all shapes and forms contrary to what is believed. Bollinger bands remain the principal indicator in the volatility bands family. K's Volatility bands is an attempt at optimizing the original bands. Below is the method of calculation:
* We must first start by calculating a rolling measure based on the average between the highest high and the lowest low in the last specified lookback window. This will give us a type of moving average that tracks the market price. The specificity here is that when the market does not make higher highs nor lower lows, the line will be flat. A flat line can also be thought of as a magnet of the price as the ranging property could hint to a further sideways movement.
* The K’s volatility bands assume the worst with volatility and thus will take the maximum volatility for a given lookback period. Unlike the Bollinger bands which will take the latest volatility calculation every single step of time, K’s volatility bands will suppose that we must be protected by the maximum of volatility for that period which will give us from time to time stable support and resistance levels.
Therefore, the difference between the Bollinger bands and K's volatility bands are as follows:
* Bollinger Bands' formula calculates a simple moving average on the closing prices while K's volatility bands' formula calculates the average of the highest highs and the lowest lows.
* Bollinger Bands' formula calculates a simple standard deviation on the closing prices while K's volatility bands' formula calculates the highest standard deviation for the lookback period.
Applying the bands is similar to applying any other volatility bands. We can list the typical strategies below:
* The range play strategy : This is the usual reversal strategy where we buy whenever the price hits the lower band and sell short whenever it hits the upper band.
* The band re-entry strategy : This strategy awaits the confirmation that the price has recognized the band and has shaped a reaction around it and has reintegrated the whole envelope. It may be slightly lagging in nature but it may filter out bad trades.
* Following the trend strategy : This is a controversial strategy that is the opposite of the first one. It assumes that whenever the upper band is surpassed, a buy signal is generated and whenever the lower band is broken, a sell signal is generated.
* Combination with other indicators : The bands can be combined with other technical indicators such as the RSI in order to have more confirmation. This is however no guarantee that the signals will improve in quality.
* Specific strategy on K’s volatility bands : This one is similar to the first range play strategy but it adds the extra filter where the trade has a higher conviction if the median line is flat. The reason for this is that a flat line means that no higher highs nor lower lows have been made and therefore, we may be in a sideways market which is a fertile ground for mean-reversion strategies.
Volatility-Volume Index (VVI)Volatility-Volume Index (VVI) – Indicator Description
The Volatility-Volume Index (VVI) is a custom trading indicator designed to identify market consolidation and anticipate breakouts by combining volatility (ATR) and trading volume into a single metric.
How It Works
Measures Volatility : Uses a 14-period Average True Range (ATR) to gauge price movement intensity.
Tracks Volume : Monitors trading activity to identify accumulation or distribution phases.
Normalization : ATR and volume are normalized using their respective 20-period Simple Moving Averages (SMA) for a balanced comparison.
Interpretation
VVI < 1: Low volatility and volume → Consolidation phase (range-bound market).
VVI > 1: Increased volatility and/or volume → Potential breakout or trend continuation.
How to Use VVI
Detect Consolidation:
Look for extended periods where VVI remains below 1.
Confirm with sideways price movement in a narrow range.
Anticipate Breakouts:
A spike above 1 signals a possible trend shift or breakout.
Why Use VVI?
Unlike traditional volatility indicators (ATR, Bollinger Bands) or volume-based tools (VWAP), VVI combines both elements to provide a clearer picture of consolidation zones and breakout potential.
Volatility GuppyBased on my previous script "Turtle N Normalized," this script plots the CM SuperGuppy on the value of N to identify changing trends in the volatility of any instrument.
Turtle rules taken from an online PDF:
"The Turtles used a concept that Richard Dennis and Bill Eckhardt called N to represent the underlying volatility of a particular market.
N is simply the 20-day exponential moving average of the True Range, which is now more commonly known as the ATR. Conceptually, N represents the average range in price movement that a particular market makes in a single day, accounting for opening gaps. N was measured in the same points as the underlying contract.
The Turtles built positions in pieces which we called Units. Units were sized so that 1 N represented 1% of the account equity. Thus, a unit for a given market or commodity can be calculated using the following formula:
Unit = 1% of Account/(N x Dollars per Point)"
To normalize the Unit formula, this script instead takes the value of (close/N). Dollars per point = 1 for stocks and crypto, but will change depending on the contract specifications for individual futures .
"Since the Turtles used the Unit as the base measure for position size, and since those units were volatility risk adjusted, the Unit was a measure of both the risk of a position, and of the entire portfolio of positions."
When the EMA's are green, volatility is decreasing.
When the EMA's are red, volatility is increasing.
When the EMA's are grey, the trend is changing.
Volatility Strategy 01a quantitative volatility strategy (especially effective in trend direction on the 15min chart on the s&p-index)
the strategy is a rule-based setup, which dynamically adapts to the implied volatility structure (vx1!–vx2!)
context-dependent mean reversion strategy based on multiple timeframes in the vix index
a signal is provided under following conditions:
1. the vvix/vix spread has deviated significantly beyond one standard deviation
2. the vix is positioned above or below 3 moving averages on 3 minor timeframes
3. the trade direction is derived from the projected volatility regime, measured via vx1! and vx2! (cboe)
Volatility & Momentum Nexus (VMN)Volatility & Momentum Nexus (VMN)
This indicator was designed to solve a common trader's problem: chart clutter from dozens of indicators that often contradict each other. The Volatility & Momentum Nexus ( VMN ) is not just another indicator; it's a complete analysis system that synthesizes four essential market pillars into a single, clean, and intuitive visual signal.
The goal of VMN is to identify high-probability moments where a period of accumulation (low volatility) is about to erupt into an explosive move, confirmed by trend, momentum, and volume.
VMN analyzes the real-time confluence of four critical elements:
The Trend (The Main Filter): A 100-period Exponential Moving Average (EMA) sets the overall context. The indicator will only look for buy signals above this line (in an uptrend) and sell signals below it (in a downtrend). The line's color changes for quick visualization.
Volatility (Energy Accumulation): Using Bollinger Bands Width (BBW), the indicator identifies "Squeeze" periods—when the price contracts and builds up energy. These zones are marked with a yellow background on the chart, signaling that a major move is imminent.
Momentum (The Trigger): An RSI (Relative Strength Index) acts as the trigger. A signal is only validated if momentum confirms the direction of the breakout (e.g., RSI > 55 for a buy), ensuring we enter the market with force.
Volume (The Final Confirmation): No breakout move is credible without volume. VMN checks if the volume at the time of the signal is significantly higher than its recent average, adding a vital layer of confirmation.
Green Arrow (Buy Signal): Appears ONLY when ALL the following conditions are met simultaneously:
Price is above the 100 EMA (Bullish Trend).
The chart is exiting a Squeeze zone (yellow background on the previous bar).
Price breaks above the upper Bollinger Band.
RSI is above the buy threshold (default 55).
Volume is above average.
Red Arrow (Sell Signal): Appears ONLY when all the opposite conditions are met.
Do not treat signals as blind commands to trade. They are high-probability confirmations.
Look for signals near key Support/Resistance levels for an even higher success rate.
Always set a Stop Loss (e.g., below the low of the signal candle or below the lower Bollinger Band for a buy).
All parameters (EMA, RSI, Bollinger Bands lengths, thresholds, etc.) can be customized from the settings menu to adapt the indicator to any financial asset or timeframe.
Disclaimer: This indicator is a tool for educational and analytical purposes. It does not constitute and should not be interpreted as financial advice. Trading involves significant risk. Always perform your own analysis and backtesting before risking real capital.
Volatility Adjusted Grid [Gann]█ OVERVIEW
Gann Square of 9 is one of the many brilliant concepts from W.D.Gann himself where it revolves around the idea that price is moving in a certain geometrical pattern. Numbers on the Square of 9 spiral tables, especially those lie in every 45degree in the chart act as key vibration levels where prices have tendency to react to (more on the table below).
There are few square of 9 related scripts here in Tradingview and while there's nothing wrong with them, it doesn't address 1 particular issue that i have: The numbers can be too rigid even when scaled based on current price because the levels are fixed, which makes them not tradable on certain timeframes depending on where the price currently sitting.
Heres 5min and 1hour Bitcoin chart to illustrate what i mean: Grey line on the left is based on Volatility Adjusted levels, while red/blue on the right are the standard Gann levels.
You can see that on 1hour chart, it provides a good levels (both Volatility Adjusted and the standard one happened to share the same multiplier in this case),
1Hour Chart:
On 5 min chart tells a different story as the range between blue/red levels can be deemed as to big for a short term trade, while the grey line is adjusted to suit that particular timeframe (You can still adjust to make it bigger/smaller from the settings, more on this below)
5Min Chart:
█ Little bit on Gann Square of 9 table
This is the square of nine table, the numbers highlighted in Red are known as Cardinal Cross and considered to be a major Support/Resistance while those in Blue color are known as Ordinal Cross considered as minor (but still important) Support/Resistance levels
Similarly, this script use these numbers (and certain multipliers) to print out the levels, with Cardinal numbers represented by solid lines and Ordinal numbers by dotted lines.
█ How it Works and Limitations
The Volatility Adjusted grid will go through several iterations of different multipliers to find the Gann number range that is at least bigger than times ATR. Because it's using ATR to determine the range, occasionally you'll notice that the line become smaller as ATR contracting (and vice versa). To overcome this, you can change the size range multiplier from the settings to retrieve the previous range size.
Use the size guide at the bottom left to find the multiplier that suits your need:
1st Row -> Previous Range -- Change Range Size to number lower than this to get a smaller range
2nd Row -> Next Range -- Change Range Size to number higher than this to get a larger range
Example:
Before:
After:
As you'll soon realise, the key here is to find the range that fits the historical structure and suits your own strategy. Enjoy :)
█ Disclaimer
Past performance is not an indicator of future results.
My opinions and research are my own and do not constitute financial advice in any way whatsoever.
Nothing published by me constitutes an investment recommendation, nor should any data or Content published by me be relied upon for any investment/trading activities.
I strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.
Any ideas to further improve this indicator are welcome :)
Volatility IndexThis indicator is based on Historical Volatility (HV) built-in indicator with minor tweaks to match the Bitcoin Volatility Index (from Bybt).
Also, you can select a symbol to compare its volatility with the volatility of the currently selected symbol.
Volatility of Volatility MA - LayeringProvides a volatility of volatility moving average to show trends in Vol of Vol. Meant to be used with Volume MA, and Volatility MA, layered on top of eachother.
Volatility IndicatorThe Volatility Index measures the market volatility by plotting a smoothed average of the True Range.
Based on HPotter's idea (),
it returns an average of the TrueRange over a specific number of bars.
Here the result is passed through the Fisher's transform and normalized to 0/1-range.
This indicator may be used to identify stretches in the price movements, suitable for entry.
Volatility Across CoinsCompare the recent volatility of 8 cryptocurrencies, based on percentage change per candle.
Useful for volatility strategies to find the highest volatility coins over recent periods or to get an at-a-glance view of volatility correlations.
Options to change the resolution and find average % change per candle over user defined length.
Key:
BTC = Yellow/Gold
ETH = Purple
LTC = Gray
NEO = Green
IOTA = Light Blue
XMR = Orange
BCH = Red
Dash = Blue
Volatility MeasureThis indicator is super simple, it gives you the average amount of volatility (IN PERCENT) in any given asset over any given timeframe over the last 100 periods. Adjustable. This is useful for gauging volatility, risk, reward, opportunity set, and more. It can help you set stop losses, tell how much risk you are actually taking based on historical measures, and how much of what you do is based on skill or luck. Enjoy!
Pholesolus
Simplest volatility bandsVolatility bands based on average candle percentage spread. Tested on BTCUSD charts only.
Based on the 68-95-99.7 rule, it seems that the spread, for daily and 4-H candles, follows a normal distribution: that means, around 85% of candles have a %-spread within sma(low/high, some_len) and sma(high/low, some_len) , and around 95% of candles within the pow2 of that range.
If you take the mean between the boundaries of the first %-spreads band, and calculate the 1.5 standard deviation of past some_len candles (I'm speaking from memory, it has been a while since I did them), the 1.5 standard deviation bands match similarly the %-spread bands, and around 85% of the candles are within these %-spread bands.
If you then take the pow2 of the bands, it will be similar to the 2 * std of the original bands, with around 95% of data within the pow2 bands.
You can take ema or other similar means with similar results, and the same for different lengths, but it seems that sma with a len of 14 is the more stable ones for both daily and 4-H, and taken other average calculations doesn't cause too many differences respect to the sma. I haven't tested too much for lower or higher timeframes.
With those %-spread bands, I multiple and divide those spreads to the open value of a new candle to get the two bands.
So, in short, you know that 85% of candles are within the closer bands, and around 95% of candles, around the bigger one. Once a new candle is born, the bands won't move (the bands are calculated from the previous candle, so the current candle's price movement doesn't move the band).
Going out the bands implies a sudden increase in volality, which usually causes rejection. They happen mostly at breakouts and ends of heavy trends. If a candle closes above the bigger band, you have probably got a breakout (a rejection rarely happens if the candle have already closed), although a breakout can happen without closing above the bands if volatility was already high.
If a trend is already stablished and is healthy, you won't probably see candles going out the bands, not even with a wick. When the trend is parabolic, and goes above the candle, the trend has probably ended, although the trend can be exhausted without going out the bands as well.
Heavy but not yet exhausted trends (specially recently started heavy downtrends), usually reach the bottom of the bigger bands during 4 o 5 contiguous candles (check visually looking at bitcoin history though, I'm speaking from memory).
So, the possibilities are multiple and you cannot use the bands to form a strategy, as usual. It can be comfortable enough psycologically for going to sleep, by moving your stop-loss to a point out of the bands in the opposite direction of your trade, and adjusting your position size accordingly; or just to check momentum looking at how close are the candle limits to the bands.
But, as usual, you are responsible of what you do with your money :)
Volatility Strategy The Volatility function measures the market volatility by plotting a
smoothed average of the True Range. It returns an average of the TrueRange
over a specific number of bars, giving higher weight to the TrueRange of
the most recent bar.
WARNING:
- This script to change bars colors.
Volatility Calculator for Daily Top and Bottom RangeWith the usage of ATR, applied on the close of the daily candle, I am calculated the volatility channels for the TOP and BOTTOM
Based on this logic, we can estimate, with a huge confidence factor, where the prices are going to be compressed for the trading day.
Having said that, lets take a look at the data gathered among the most important financial markets:
SPX
TOP CROSSES : 2116
BOT CROSSES : 1954
Total Daily Candles : 18908
Occurance ratio = 0.215
NDX
TOP CROSSES : 1212
BOT CROSSES : 1183
Total Daily Candles : 9386
Occurance ratio = 0.255
DIA
TOP CROSSES : 759
BOT CROSSES : 769
Total Daily Candles : 6109
Occurance ratio = 0.25
DXY
TOP CROSSES : 1597
BOT CROSSES : 1598
Total Daily Candles : 13156
Occurance ratio = 0.243
DAX
TOP CROSSES : 1878
BOT CROSSES : 1848
Total Daily Candles : 13155
Occurance ratio = 0.283
BTC USD
TOP CROSSES : 416
BOT CROSSES : 417
Total Daily Candles : 4290
Occurance ratio = 0.194
ETH USD
TOP CROSSES : 247
BOT CROSSES : 268
Total Daily Candles : 2452
Occurance ratio = 0.21
EUR USD
TOP CROSSES : 820
BOT CROSSES : 805
Total Daily Candles : 7489
Occurance ratio = 0.217
GOLD
TOP CROSSES : 1722
BOT CROSSES : 1569
Total Daily Candles : 13747
Occurance ratio = 0.239
USOIL
TOP CROSSES : 1077
BOT CROSSES : 1089
Total Daily Candles : 10231
Occurance ratio = 0.212
US 10Y
TOP CROSSES : 1302
BOT CROSSES : 1365
Total Daily Candles : 9075
Occurance ratio = 0.294
Based on this, we can assume with a very high confidence ( 70-80%) that the market is going to stay, within the range created from the BOT and TOP ATR points.
Volatility Adapted Relative StrengthVARS uses a stock's ALPHA in comparison to the SPX to determine whether there is RS on an volatility adjusted basis.
Volatility Quality Histogram (NicoadW)This indicator is based on the Volatility Quality Index ( VQI ) by Thomas Stridsman.
It shows the slope of the VQI in form of a histogram.
The VQI is calculated in the following Steps:
1. Applying a WeightedMovingAverage with the onto the low, open, high, close and prior close
2. Calculating the true range and the range from the results of step 1 and setting them into relation
3. Calculating a weighted lumpsum of the results from step 2 (This is the value of the widely known VQI )
4. The change from the current value of step 3 compared to the last value is the VQI-Slope
5. The VQI-Slope is filtered by only recogning changes greater as the User-Input
6. If the Slope is rising, its considered a long trend and if its falling its considered a short trend
User Inputs
Valuation Period: Length of the WMAs
Filter in Pips: minimum change of the VQI-Slope to result in a Trend-Change
Visuals
Inrease the size of the Signal: Highligts the Trend-Changes
Color Chart Bars: Colors the bars of the main chart depending on the trend given by the VQI
Volatility OscillatorThis tool displays relative volatility and directional trend. Excellent way to pickup diversions and reversals. Length can be lowered to 11 or 13 in settings to show price range.
Can be used to identify patterns such as parallel channels and likely direction of price action as pictured below.
