TheATR™: Volatility Extremes (VolEx)Volatility is a crucial aspect of financial markets that is closely monitored by traders and investors alike. The traditional Average True Range (ATR) oscillator is a widely used technical indicator for measuring volatility in financial markets. However, there are limitations to the ATR oscillator, as it does not account for changing market conditions and may not adequately reflect extreme price movements. To address these limitations, TheATR has developed the VolEx indicator, which aims to identify extremes in the ATR oscillator by building dynamic thresholds using either a 'percentage' or 'standard deviation' based comparison with the value of the ATR.
The VolEx indicator utilizes a dynamic approach to measure volatility by considering the current level of the ATR oscillator relative to the dynamically generated thresholds. The dynamic thresholds are calculated based on the current ATR value and the chosen method of comparison (either 'percentage' or 'standard deviation'). If the ATR value exceeds the upper dynamic threshold, the market is experiencing high volatility, while a value below the lower dynamic threshold indicates low volatility.
The VolEx indicator offers several advantages over traditional volatility indicators, such as the ATR oscillator. First, it takes into account the changing market conditions and adjusts the thresholds accordingly. Second, it offers flexibility in the choice of the comparison method, allowing traders to tailor the indicator to their specific trading strategies. Finally, it provides clear signals for identifying extremes in volatility, which can be used to inform trading decisions.
In summary, the VolEx indicator developed by TheATR is a dynamic and flexible technical indicator that offers a robust approach to measuring volatility in financial markets. By utilizing dynamic thresholds and allowing for different comparison methods, the VolEx indicator provides a valuable tool for traders and investors seeking to identify extremes in market volatility..
NOTE: It is important to note that volatility, as measured by the VolEx indicator, does not provide any directional bias for the market movement. Rather, it simply indicates the degree to which the market is moving, regardless of direction. Traders and investors must use other technical or fundamental analysis tools to determine the direction of the market and make informed trading decisions based on their individual strategies and risk tolerance.
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Volatility Spike EstimatorPlots the Average True Range (ATR), its historical mean, the upper threshold for a volatility spike, and uses background color to show the likelihood of a volatility spike based on the current ATR value.
Green background indicates an increased likelihood of a volatility spike, while red background means a spike might have already occurred or be in progress.
Update: In this version, we added a short-term ATR calculation with an adjustable input parameter, shortTermATRLength. The likelihood of a volatility spike is now estimated based on the short-term ATR instead of the original ATR. This change makes the indicator more sensitive to recent market conditions and can help detect potential volatility spikes more quickly.
Volatility-Based Mean Reversion BandsThe Volatility-Based Mean Reversion Bands indicator is a powerful tool designed to identify potential mean reversion trading opportunities based on market volatility. The indicator consists of three lines: the mean line, upper band, and lower band. These bands dynamically adjust based on the average true range (ATR) and act as reference levels for identifying overbought and oversold conditions.
The calculation of the indicator involves several steps. The average true range (ATR) is calculated using a specified lookback period. The ATR measures the market's volatility by considering the range between high and low prices over a given period. The mean line is calculated as a simple moving average (SMA) of the closing prices over the same lookback period. The upper band is derived by adding the product of the ATR and a multiplier to the mean line, while the lower band is derived by subtracting the product of the ATR and the same multiplier from the mean line.
Interpreting the indicator is relatively straightforward. When the price approaches or exceeds the upper band, it suggests that the market is overbought and may be due for a potential reversal to the downside. On the other hand, when the price approaches or falls below the lower band, it indicates that the market is oversold and may be poised for a potential reversal to the upside. Traders can look for opportunities to enter short positions near the upper band and long positions near the lower band, anticipating the price to revert back towards the mean line.
The bar color and background color play a crucial role in visualizing the indicator's signals and market conditions. Lime-colored bars are used when the price is above the upper band, indicating a potential bearish mean reversion signal. Conversely, fuchsia-colored bars are employed when the price is below the lower band, suggesting a potential bullish mean reversion signal. This color scheme helps traders quickly identify the prevailing market condition and potential reversal zones. The background color complements the bar color by providing further context. Lime-colored background indicates a potential bearish condition, while fuchsia-colored background suggests a potential bullish condition. The transparency level of the background color is set to 80% to avoid obscuring the price chart while still providing a visual reference.
To provide additional confirmation for mean reversion setups, the indicator incorporates the option to use the Relative Strength Index (RSI) as a confluence factor. The RSI is a popular momentum oscillator that measures the speed and change of price movements. When enabled, the indicator checks if the RSI is in overbought territory (above 70) or oversold territory (below 30), providing additional confirmation for potential mean reversion setups.
In addition to visual signals, the indicator includes entry arrows above or below the bars to highlight the occurrence of short or long entries. When the price is above the upper band and the confluence condition is met, a fuchsia-colored triangle-up arrow is displayed above the bar, indicating a potential short entry signal. Similarly, when the price is below the lower band and the confluence condition is met, a lime-colored triangle-down arrow is displayed below the bar, indicating a potential long entry signal.
Traders can customize the indicator's parameters according to their trading preferences. The "Lookback Period" determines the number of periods used in calculating the mean line and the average true range (ATR). Adjusting this parameter can affect the sensitivity and responsiveness of the indicator. Smaller values make the indicator more reactive to short-term price movements, while larger values smooth out the indicator and make it less responsive to short-term fluctuations. The "Multiplier" parameter determines the distance between the mean line and the upper/lower bands. Increasing the multiplier widens the bands, indicating a broader range for potential mean reversion opportunities, while decreasing the multiplier narrows the bands, indicating a tighter range for potential mean reversion opportunities.
It's important to note that the Volatility-Based Mean Reversion Bands indicator is not a standalone trading strategy but rather a tool to assist traders in identifying potential mean reversion setups. Traders should consider using additional analysis techniques and risk management strategies to make informed trading decisions. Additionally, the indicator's performance may vary across different market conditions and instruments, so it's advisable to conduct thorough testing and analysis before integrating it into a trading strategy.
Volatility patterns / quantifytools- Overview
Volatility patterns detect various forms of indecisive price action, on a larger scale as a compressed range and on a smaller scale as indecision candles. Indecisive and volatility suppressing price action can be thought of as a spring being pressed down. The more suppression, the more tension is built and eventually released as a spike or series of spikes in volatility. Each volatility pattern is assigned an influence period, during which average and peak relative volatility is recorded and stored to volatility metrics.
- Patterns
The following scenarios are qualified as indecision candles: inside candles, indecision engulfing candles and volatility shifts.
By default, each indecision candle is considered a valid pattern only when another indecision candle has taken place within 3 periods, e.g. prior inside candle + indecision engulfing candle = valid volatility pattern. This measurement is taken to filter noise by looking for multiple hints of pending volatility, rather than just one. Level of tolerated noise can be changed via input menu by using sensitivity setting, by default set to 2.
Sensitivity at 1: Any single indecision candle is considered a valid pattern
Sensitivity at 2: 2 indecision candles within 3 bars is considered a valid pattern
Sensitivity at 3: 2 indecision candles within 2 bars (consecutive) is considered a valid pattern
The following scenarios are qualified as range patterns: series of lower highs/higher lows and series of low volatility pivots.
A pivot is defined by highest/lowest point in price, by default within 2 periods back and 2 periods forward. When 4 pivots with qualities mentioned above are found, a box indicating compressed range will appear. Both required pivots and pivot definition can be adjusted via input menu.
- Influence time and metrics
By default, influence time for each volatility pattern is set to 6 candles, a period for which spike(s) in volatility is expected. For each influence period, average relative volatility (volatility relative to volatility SMA 20) and peak relative volatility is recorded and stored to volatility metrics. All metrics used in calculations are visible in "Data Window "tab. Average and peak volatility during influence period will vary depending on chart, timeframe and chosen settings. Tweaking the settings might result in an improvement and is worth experimenting with.
- Visuals
By default, indecision candles are visualized as yellow lines and range patterns as orange boxes. Influence time periods are respectively visualized as colored candle borders, applied as long as influence time period is active. All colors are fully customizable via input menu.
- Practical guide
Volatility patterns depict moments of equal strength from both bulls and bears. While this equilibrium is in place, price is stagnant and compresses until either side initiates volatility, releasing the built up tension. On top of hedging and playing the volatility using volatility based instruments, some other methods can be applied to take advantage of the somewhat tricky areas of indecision.
Example #1: Trading volatility
Volatility is not a bad thing from a trading perspective, but can actually be fertile ground for executing trade setups. Trading volatility influence periods from higher timeframes on lower timeframes gives greater resolution to work with and opportunities to take advantage of the wild swings created.
Example #2: Finding bias for patterns
Points of confluence where it anyway makes sense to favor one side over the other can be used for establishing bias for indecisive price action as well. At face value, it makes sense to expect bearish reactions at range highs and bullish reactions at range low, for which volatility patterns can provide a catalyst.
Example #3: Betting on initiation direction
Betting on direction of the first volatile move can easily go against you, but if risk/reward is able to compensate for the poor win rate, it's a valid idea to consider and explore.
Volatility SpeedometerThe Volatility Speedometer indicator provides a visual representation of the rate of change of volatility in the market. It helps traders identify periods of high or low volatility and potential trading opportunities. The indicator consists of a histogram that depicts the volatility speed and an average line that smoothes out the volatility changes.
The histogram displayed by the Volatility Speedometer represents the rate of change of volatility. Positive values indicate an increase in volatility, while negative values indicate a decrease. The height of the histogram bars represents the magnitude of the volatility change. A higher histogram bar suggests a more significant change in volatility.
Additionally, the Volatility Speedometer includes a customizable average line that smoothes out the volatility changes over the specified lookback period. This average line helps traders identify the overall trend of volatility and its direction.
To enhance the interpretation of the Volatility Speedometer, color zones are used to indicate different levels of volatility speed. These color zones are based on predefined threshold levels. For example, green may represent high volatility speed, yellow for moderate speed, and fuchsia for low speed. Traders can customize these threshold levels based on their preference and trading strategy.
By monitoring the Volatility Speedometer, traders can gain insights into changes in market volatility and adjust their trading strategies accordingly. For example, during periods of high volatility speed, traders may consider employing strategies that capitalize on price swings, while during low volatility speed, they may opt for strategies that focus on range-bound price action.
Adjusting the inputs of the Volatility Speedometer indicator can provide valuable insights and flexibility to traders. By modifying the inputs, traders can customize the indicator to suit their specific trading style and preferences.
One input that can be adjusted is the "Lookback Period." This parameter determines the number of periods considered when calculating the rate of change of volatility. Increasing the lookback period can provide a broader perspective of volatility changes over a longer time frame. This can be beneficial for swing traders or those focusing on longer-term trends. On the other hand, reducing the lookback period can provide more responsiveness to recent volatility changes, making it suitable for day traders or those looking for short-term opportunities.
Another adjustable input is the "Volatility Measure." In the provided code, the Average True Range (ATR) is used as the volatility measure. However, traders can choose other volatility indicators such as Bollinger Bands, Standard Deviation, or custom volatility measures. By experimenting with different volatility measures, traders can gain a deeper understanding of market dynamics and select the indicator that best aligns with their trading strategy.
Additionally, the "Thresholds" inputs allow traders to define specific levels of volatility speed that are considered significant. Modifying these thresholds enables traders to adapt the indicator to different market conditions and their risk tolerance. For instance, increasing the thresholds may highlight periods of extreme volatility and help identify potential breakout opportunities, while lowering the thresholds may focus on more moderate volatility shifts suitable for range trading or trend-following strategies.
Remember, it is essential to combine the Volatility Speedometer with other technical analysis tools and indicators to make informed trading decisions.
Volatility % (Standard Deviation of Returns)This script takes closing prices of candles to measure the Standard Deviation (σ) which is then used to calculate the volatility by taking the stdev of the last 30 candles and multiplying it by the root of the trading days in a year, month and week. It then multiplies that number by 100 to show a percentage.
Default settings are annual volatility (252 candles, red), monthly volatility (30 candles, blue) and weekly volatility (5 candles, green) if you use daily candles. It is open source so you can increase the number of candles with which the stdev is calculated, and change the number of the root that multiplies the stdev.
Volatility MeterThe title is quite self explanatory. It is a Volatility Meter based on Bollinger Bands Width and MA.
These parameters can be selected by users in settings tab:
- Basic Bollinger Bands Parameters: length , standart deviation and source
- Timeframes: 1h, 2h, 3h and 4h
If the indicator is red it means volatility is high in the market. If it is white , it means that market is not volatile right now.
Volatility BandsWe used Marc Chaikin’s Chaikin Volatility as somewhat of a baseline for this indicator and then built on it. Like Chaikin Volatility, our indicator draws primarily upon high-low spreads to quantify a security’s volatility. It also has similarities to Keltner Channels as it uses ATR rather than standard deviations in its calculation of the different bands.
Inputs:
int ‘len0’, lookback window for fast EMA of high-low ranges.
int ‘len1’, lookback window for slow EMA of high-low ranges.
int ‘len2’, lookback window for slow EMA of closing prices.
float ‘m0’, ATR multiplier for first upper and lower volatility bands.
float ‘m1’, ATR multiplier for second upper and lower volatility bands.
float ‘m2’, ATR multiplier for third upper and lower volatility bands.
int ‘lenATR’, window length for ATR calculation.
Output: 3 Upper and Lower Volatility Bands (6 total).
1. Compute High Low Spread for current period.
hlr = (high – low)
2. Calculate Exponential Moving Average of HLR at length len0
fastEMA = ema(hlr, len0)
3. Calculate Exponential Moving average of HLR at length len1 (where len1 > len0)
slowEMA = ema(hlr, len1)
4. Get EMA of closing prices at length len2(where len2 > len1 and len1 > len0)
priceEMA = ema(close, len2)
5. Use adjusted Chaikin Volatility Formula to quantify volatility
v = (fastEMA – slowEMA) / slowEMA
6. Calculate three upper and three lower volatility bands (6 total):
ex:
upper0 = priceEMA + ((1 – cv) * (atrMult0 * atr(lenATR)))
lower0 = priceEMA – ((1 – cv) * (atrMult0 * atr(lenATR)))
One possible way to use this indicator is to enter a long position when the security’s price falls below the lowest volatility band and then exit when it crosses above the third upper band. This seems to get the best results for quick, high frequency trading. Another approach is to enter a position when the bands begin to break out from a compact state and the width between them increases.
Still tweaking the idea, so any feedback would be appreciated.
Volatility ScreenerA screener to find easily volatile coins. The method is based on the standard deviation to the price average ratio.
Choose your target coins (coin1 to coin 15) and selected the length of observation.
By choose the minimum volatility level you can distinguish the coins with high volatility.
Change the timeframe to your desired.
Volatility-Weighted Moving Average SystemThis simple script creates a moving average system weighted by volatility. The moving averages are less sensitive to price action than the typical moving averages we use, and their crossovers can be used to identify extended trends.
I've colored the background depending on trend. Ideally in the future, I'll draw long or short signals on-chart depending on the width between the bands, which works as a faster indicator of trend-change than crossover does.
Hope you all enjoy. Happy holidays.
Volatility Pivot Support and Resistance [Loxx]Volatility Pivot Support and Resistance calculates "pivots" (support/resistance lines) based on current symbol/timeframe Average True Range calculated volatility.
What is 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.
Included:
-Bar coloring
Volatility BalanceThis "Volatility Balance" indicator measures volatility from a different perspective.
It plots both an upside (green) and downside (red) volatility rating as well as a histogram depicting either the difference between the two or the two combined.
First, it calculates the ratio of the candle body (open to close) in relation to the entire candle range (high - low). This is the "candle body size ratio".
Secondly, it calculates the "dominance ratio" of the upside volatility (close - low) or downside volatility (high - close), in relation to the entire candle range (high - low)
Thirdly, it then uses the above values to derive the upside and downside power.
The upside and downside powers are calculated by multiplying the "candle body size ratio" to the upside or downside "dominance ratio" and then to the entire candle range.
It then divides this value by the close of the candle to normalize the overall value. After which it then multiplies this final value by 100, to get a percentage value between 0 - 100%.
It then averages the upside or downside "power value", using a volume-weighted moving average with the averaging period initially set to 7 periods, but this can be altered in the format settings.
The volatility difference or volatility size is displayed as a histogram style behind the green and red upside and downside power lines (depending on which value the user chooses), via an option in the format settings.
The volatility difference is the upside minus the downside and the volatility size is the upside and downside added together.
In case the user wishes to see EACH candle's power value before it has been averaged with volume, there is an option in the format settings to show each candle's upside or downside "power". If this method is chosen, the histogram will be hidden. There are also various check boxes, in case the user wishes to hide any of the plotted values.
The volatility size can determine how much energy has been behind the change in price over the period measured.
The volatility difference can determine how much upwards or downward energy is present within the period measured, in other words, the possible direction.
The use of the candle body ratio helps to determine the balance between upwards and downwards energy. If it is small, then it implies that there is more of a balance between buyers and sellers and therefore less volatility, as the price hasn't moved as much than if the body of the candle was larger. Smaller values indicate that price may not move as much and therefore lock up your funds longer until price starts to move again.
The use of a volume-weighted moving average helps to give higher volume periods more significance towards a possible price shift.
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This indicator was originally inspired by the Profit Trailer Feeder add-on and it's volatility groupings.
If you don't already have the Profit Trailer Feeder add-on to the Profit Trailer software, and you have now decided to get it, please help me out and use my affiliate link below. It won't cost you any extra, but it helps keep the lights on, so I can create more of these indicators.
Kindest Regards
Created 2018 - by @Squiggles#8806
LTC Tip Jar: LX4bUBdjiRPsagUbZoAczSGgDC4YqTJL7w
Profit Trailer Feeder affiliate link: cryptoprofitbot.com
VolatilityDivergenceRedGreen by STTAName: VolatilityDivergenceRedGreen by STTA
- Underlying and implied volatiliy normally show negative correlated behavior (price rises, vola falls and vice versa)
- This study shows symbols in on candles in chart where Undelying and corresponding vola index show same bahvior for 1,2 or 3 consecutive bars. (price rises and vola rises and vice versa)
- This situation is called Vola Divergence. Red, when prices and vola fall; green, when price and vola rise
- This information can be used to detect possible end of Up/Down-Swings.
- User can configure if rising or falling or both price movements shall be displayed.
- This study can be used with root symbols, which provide corresponding volatility indices.
- supported Root Symbols: SPX, NDQ, DJI, RUT, CL, XLE, GC, SI, EUR, HSI, FXI, EWZ, AMZN, AAPL, GS, GOOG, IBM, DEU40
- in all other symbols, no symbols are displayed.
Inputs
- underlying displayed in chart
Settings/Parameter
- each Divergence can be switched off/on separately
- output of each displayed symbol can be configured
Outputs
- RedDiv1: first bar with rising price and rising volatility index
- GreenDiv1: first bar with falling price and falling volatility index
- RedDiv2: second bar in a row with rising price and rising volatility index
- GreenDiv2: second bar with falling price and falling volatility index
- RedDiv3: third bar in a row with rising price and rising volatility index
- GreenDiv3: third bar in a row with falling price and falling volatility index
Volatility Footprint CandlesVolatility Footprint is an innovative volume profile indicator that dynamically adapts to real-time market conditions, providing traders with a powerful tool to visualize and interpret market structure, order flow, and potential areas of support and resistance.
At its core, Volatility Footprint combines the concepts of market profile, volume analysis, and volatility measurement to create a unique and adaptive charting experience. The indicator intelligently adjusts its display based on the current market volatility, ensuring that traders always have a clear and readable chart, regardless of the instrument or timeframe they are analyzing.
The footprint chart is composed of a series of color-coded boxes, each representing a specific price level. The color of the box indicates whether there is a net buying or selling pressure at that level, while the opacity reflects the relative strength of the volume. This intuitive visualization allows traders to quickly identify areas of high and low volume, as well as potential imbalances in order flow.
In addition to the individual box volumes, Volatility Footprint also calculates and displays the cumulative volume delta. This running total of buy and sell volumes across all price levels provides valuable insight into the overall market sentiment and potential trends.
One of the key features of Volatility Footprint is its ability to identify and highlight the Point of Control (POC). The POC represents the price level with the highest volume concentration and serves as a key reference point for potential support or resistance. By drawing attention to this crucial level, the indicator helps traders make more informed decisions about potential entry and exit points.
Volatility Footprint is designed to be highly customizable, allowing traders to tailor the appearance of the footprint chart to their specific preferences. Users can easily modify the colors, opacity, and size of the boxes, labels, and POC marker to enhance readability and clarity.
The indicator's versatility makes it suitable for a wide range of trading styles and strategies. Whether you are a scalper looking for short-term opportunities or a swing trader aiming to identify potential trend reversals, Volatility Footprint can provide valuable insights into market dynamics.
By combining Volatility Footprint with other forms of analysis, such as price action, key levels, and technical indicators, traders can gain a more comprehensive understanding of market behavior and make better-informed trading decisions.
Volatility Footprint's adaptive approach to volume profile analysis sets it apart from traditional fixed-resolution volume profile indicators. By dynamically adjusting to the unique characteristics of each instrument and timeframe, the indicator ensures that traders always have a clear and meaningful representation of market structure and order flow.
Volatility Footprint is a powerful tool that traders can incorporate into their market analysis and decision-making process. By providing a dynamic, visual representation of volume and order flow at different price levels, this indicator offers valuable insights into market structure, sentiment, and potential areas of support and resistance. Let's explore how traders might effectively utilize Volatility Footprint in their trading approach.
1. Identifying Key Levels:
One of the primary uses of Volatility Footprint is to identify key price levels where significant trading activity has occurred. The color-coded boxes allow traders to quickly spot areas of high volume concentration, which may indicate potential support or resistance zones. For example, if a trader notices a cluster of boxes with high opacity at a specific price level, they may interpret this as a strong support or resistance area, depending on the prevailing market context. By paying attention to these key levels, traders can make more informed decisions about potential entry and exit points, as well as placement of stop-loss orders and profit targets.
2. Assessing Market Sentiment:
The cumulative volume delta feature of Volatility Footprint provides traders with a valuable gauge of overall market sentiment. By analyzing the running total of buy and sell volumes across all price levels, traders can gain insight into the dominant market forces at play. If the cumulative delta is significantly positive, it may suggest a bullish sentiment, as buying pressure has been consistently outpacing selling pressure. Conversely, a negative cumulative delta may indicate a bearish sentiment. Traders can use this information to confirm or question their bias and adjust their trading plan accordingly.
3. Confirming Breakouts and Trend Reversals:
Volatility Footprint can be particularly useful in confirming the strength and validity of breakouts and potential trend reversals. When a price level is breached, traders can refer to the footprint chart to assess the volume and order flow characteristics around that level. If the breakout is accompanied by a surge in volume and a clear imbalance between buying and selling pressure, it may suggest a strong and sustainable move. On the other hand, if the volume is relatively low or evenly distributed, the breakout may be less reliable. By using Volatility Footprint to confirm breakouts, traders can make more informed decisions about whether to enter or exit a trade, or to adjust their position size.
4. Detecting Imbalances and Potential Reversals:
Imbalances between buying and selling pressure at specific price levels can often precede significant market moves or reversals. Volatility Footprint makes it easy for traders to spot these imbalances visually. For instance, if a trader observes a price level with a significantly larger number of sell boxes compared to buy boxes, it may indicate a potential exhaustion point for a bullish trend, and a reversal might be imminent. Traders can use this information in conjunction with other technical analysis tools, such as trendlines, moving averages, or momentum oscillators, to identify high-probability trading opportunities.
5. Adapting to Market Conditions:
One of the key strengths of Volatility Footprint is its ability to dynamically adapt to the unique volatility characteristics of different instruments and timeframes. This adaptability ensures that the indicator remains relevant and informative across a wide range of market conditions. Traders can use Volatility Footprint to gauge the relative volatility and volume of a particular instrument or timeframe, and adjust their trading approach accordingly. For example, in a highly volatile market, traders may opt for wider stop-loss levels and smaller position sizes to account for the increased risk.
Incorporating Volatility Footprint into a trading strategy requires a combination of technical analysis, market understanding, and risk management. Traders should use this indicator as part of a comprehensive approach, combining it with other forms of analysis, such as price action, key levels, and technical indicators. By doing so, traders can gain a more complete picture of market dynamics and make better-informed trading decisions.
It's important to note that while Volatility Footprint provides valuable insights, it should not be relied upon as a standalone trading signal. Traders should always consider the broader market context, their risk tolerance, and their overall trading plan when making decisions based on the information provided by this indicator.
In conclusion, Volatility Footprint offers traders a dynamic and visually intuitive way to analyze market structure, volume, and order flow. By identifying key levels, assessing market sentiment, confirming breakouts, detecting imbalances, and adapting to market conditions, traders can leverage this powerful tool to make more informed and confident trading decisions. As with any technical analysis tool, Volatility Footprint should be used in conjunction with sound risk management principles and a well-defined trading strategy to maximize its effectiveness.
Volatility Zones (STDEV %)This indicator displays the relative volatility of an asset as a percentage, based on the standard deviation of price over a custom length.
🔍 Key features:
• Uses standard deviation (%) to reflect recent price volatility
• Classifies volatility into three zones:
Low volatility (≤2%) — highlighted in blue
Medium volatility (2–4%) — highlighted in orange
High volatility (>4%) — highlighted in red
• Supports visual background shading and colored line output
• Works on any timeframe and asset
📊 This tool is useful for identifying low-risk entry zones, periods of expansion or contraction in price behavior, and dynamic market regime changes.
You can adjust the STDEV length to suit your strategy or timeframe. Best used in combination with your entry logic or trend filters.
Z-Score Normalized Volatility IndicesVolatility is one of the most important measures in financial markets, reflecting the extent of variation in asset prices over time. It is commonly viewed as a risk indicator, with higher volatility signifying greater uncertainty and potential for price swings, which can affect investment decisions. Understanding volatility and its dynamics is crucial for risk management and forecasting in both traditional and alternative asset classes.
Z-Score Normalization in Volatility Analysis
The Z-score is a statistical tool that quantifies how many standard deviations a given data point is from the mean of the dataset. It is calculated as:
Z = \frac{X - \mu}{\sigma}
Where X is the value of the data point, \mu is the mean of the dataset, and \sigma is the standard deviation of the dataset. In the context of volatility indices, the Z-score allows for the normalization of these values, enabling their comparison regardless of the original scale. This is particularly useful when analyzing volatility across multiple assets or asset classes.
This script utilizes the Z-score to normalize various volatility indices:
1. VIX (CBOE Volatility Index): A widely used indicator that measures the implied volatility of S&P 500 options. It is considered a barometer of market fear and uncertainty (Whaley, 2000).
2. VIX3M: Represents the 3-month implied volatility of the S&P 500 options, providing insight into medium-term volatility expectations.
3. VIX9D: The implied volatility for a 9-day S&P 500 options contract, which reflects short-term volatility expectations.
4. VVIX: The volatility of the VIX itself, which measures the uncertainty in the expectations of future volatility.
5. VXN: The Nasdaq-100 volatility index, representing implied volatility in the Nasdaq-100 options.
6. RVX: The Russell 2000 volatility index, tracking the implied volatility of options on the Russell 2000 Index.
7. VXD: Volatility for the Dow Jones Industrial Average.
8. MOVE: The implied volatility index for U.S. Treasury bonds, offering insight into expectations for interest rate volatility.
9. BVIX: Volatility of Bitcoin options, a useful indicator for understanding the risk in the cryptocurrency market.
10. GVZ: Volatility index for gold futures, reflecting the risk perception of gold prices.
11. OVX: Measures implied volatility for crude oil futures.
Volatility Clustering and Z-Score
The concept of volatility clustering—where high volatility tends to be followed by more high volatility—is well documented in financial literature. This phenomenon is fundamental in volatility modeling and highlights the persistence of periods of heightened market uncertainty (Bollerslev, 1986).
Moreover, studies by Andersen et al. (2012) explore how implied volatility indices, like the VIX, serve as predictors for future realized volatility, underlining the relationship between expected volatility and actual market behavior. The Z-score normalization process helps in making volatility data comparable across different asset classes, enabling more effective decision-making in volatility-based strategies.
Applications in Trading and Risk Management
By using Z-score normalization, traders can more easily assess deviations from the mean in volatility, helping to identify periods when volatility is unusually high or low. This can be used to adjust risk exposure or to implement volatility-based trading strategies, such as mean reversion strategies. Research suggests that volatility mean-reversion is a reliable pattern that can be exploited for profit (Christensen & Prabhala, 1998).
References:
• Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2012). Realized volatility and correlation dynamics: A long-run approach. Journal of Financial Economics, 104(3), 385-406.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125-150.
• Whaley, R. E. (2000). Derivatives on market volatility and the VIX index. Journal of Derivatives, 8(1), 71-84.
Volatility IndicatorThe volatility indicator presented here is based on multiple volatility indices that reflect the market’s expectation of future price fluctuations across different asset classes, including equities, commodities, and currencies. These indices serve as valuable tools for traders and analysts seeking to anticipate potential market movements, as volatility is a key factor influencing asset prices and market dynamics (Bollerslev, 1986).
Volatility, defined as the magnitude of price changes, is often regarded as a measure of market uncertainty or risk. Financial markets exhibit periods of heightened volatility that may precede significant price movements, whether upward or downward (Christoffersen, 1998). The indicator presented in this script tracks several key volatility indices, including the VIX (S&P 500), GVZ (Gold), OVX (Crude Oil), and others, to help identify periods of increased uncertainty that could signal potential market turning points.
Volatility Indices and Their Relevance
Volatility indices like the VIX are considered “fear gauges” as they reflect the market’s expectation of future volatility derived from the pricing of options. A rising VIX typically signals increasing investor uncertainty and fear, which often precedes market corrections or significant price movements. In contrast, a falling VIX may suggest complacency or confidence in continued market stability (Whaley, 2000).
The other volatility indices incorporated in the indicator script, such as the GVZ (Gold Volatility Index) and OVX (Oil Volatility Index), capture the market’s perception of volatility in specific asset classes. For instance, GVZ reflects market expectations for volatility in the gold market, which can be influenced by factors such as geopolitical instability, inflation expectations, and changes in investor sentiment toward safe-haven assets. Similarly, OVX tracks the implied volatility of crude oil options, which is a crucial factor for predicting price movements in energy markets, often driven by geopolitical events, OPEC decisions, and supply-demand imbalances (Pindyck, 2004).
Using the Indicator to Identify Market Movements
The volatility indicator alerts traders when specific volatility indices exceed a defined threshold, which may signal a change in market sentiment or an upcoming price movement. These thresholds, set by the user, are typically based on historical levels of volatility that have preceded significant market changes. When a volatility index exceeds this threshold, it suggests that market participants expect greater uncertainty, which often correlates with increased price volatility and the possibility of a trend reversal.
For example, if the VIX exceeds a pre-determined level (e.g., 30), it could indicate that investors are anticipating heightened volatility in the equity markets, potentially signaling a downturn or correction in the broader market. On the other hand, if the OVX rises significantly, it could point to an upcoming sharp movement in crude oil prices, driven by changing market expectations about supply, demand, or geopolitical risks (Geman, 2005).
Practical Application
To effectively use this volatility indicator in market analysis, traders should monitor the alert signals generated when any of the volatility indices surpass their thresholds. This can be used to identify periods of market uncertainty or potential market turning points across different sectors, including equities, commodities, and currencies. The indicator can help traders prepare for increased price movements, adjust their risk management strategies, or even take advantage of anticipated price swings through options trading or volatility-based strategies (Black & Scholes, 1973).
Traders may also use this indicator in conjunction with other technical analysis tools to validate the potential for significant market movements. For example, if the VIX exceeds its threshold and the market is simultaneously approaching a critical technical support or resistance level, the trader might consider entering a position that capitalizes on the anticipated price breakout or reversal.
Conclusion
This volatility indicator is a robust tool for identifying market conditions that are conducive to significant price movements. By tracking the behavior of key volatility indices, traders can gain insights into the market’s expectations of future price fluctuations, enabling them to make more informed decisions regarding market entries and exits. Understanding and monitoring volatility can be particularly valuable during times of heightened uncertainty, as changes in volatility often precede substantial shifts in market direction (French et al., 1987).
References
• Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.
• Whaley, R. E. (2000). Derivatives on Market Volatility. Journal of Derivatives, 7(4), 71-82.
• Pindyck, R. S. (2004). Volatility and the Pricing of Commodity Derivatives. Journal of Futures Markets, 24(11), 973-987.
• Geman, H. (2005). Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. John Wiley & Sons.
• Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
• French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
Volatility Compression BreakoutThe Volatility Compression Breakout indicator is designed to identify periods of low volatility followed by potential breakout opportunities in the market. It aims to capture moments when the price consolidates within a narrow range, indicating a decrease in volatility, and anticipates a subsequent expansion in price movement. This indicator can be applied to any financial instrument and timeframe.
When the close price is above both the Keltner Middle line and the Exponential Moving Average (EMA), the bars are colored lime green, indicating a potential bullish market sentiment. When the close price is positioned above the Keltner Middle but below the EMA, or below the Keltner Middle but above the EMA, the bars are colored yellow, signifying a neutral or indecisive market condition. Conversely, when the close price falls below both the Keltner Middle and the EMA, the bars are colored fuchsia, suggesting a potential bearish market sentiment.
Additionally, the coloration of the Keltner Middle line and the EMA provides further visual cues for assessing the trend. When the close price is above the Keltner Middle, the line is colored lime green, indicating a bullish trend. Conversely, when the close price is below the Keltner Middle, the line is colored fuchsia, highlighting a bearish trend. Similarly, the EMA line is colored lime green when the close price is above it, representing a bullish trend, and fuchsia when the close price is below it, indicating a bearish trend.
Parameters
-- Compression Period : This parameter determines the lookback period used to calculate the volatility compression. A larger value will consider a longer historical period for volatility analysis, potentially capturing broader market conditions. Conversely, a smaller value focuses on more recent price action, providing a more responsive signal to current market conditions.
-- Compression Multiplier : The compression multiplier is a factor applied to the Average True Range (ATR) to determine the width of the Keltner Channels. Increasing the multiplier expands the width of the channels, allowing for a larger price range before a breakout is triggered. Decreasing the multiplier tightens the channels and requires a narrower price range for a breakout signal.
-- EMA Period : This parameter sets the period for the Exponential Moving Average (EMA), which acts as a trend filter. The EMA helps identify the overall market trend and provides additional confirmation for potential breakouts. Adjusting the period allows you to capture shorter or longer-term trends, depending on your trading preferences.
How Changing Parameters Can Be Beneficial
Modifying the parameters allows you to adapt the indicator to different market conditions and trading styles. Increasing the compression period can help identify broader volatility patterns and major market shifts. On the other hand, decreasing the compression period provides more precise and timely signals for short-term traders.
Adjusting the compression multiplier affects the width of the Keltner Channels. Higher multipliers increase the breakout threshold, filtering out smaller price movements and providing more reliable signals during significant market shifts. Lower multipliers make the indicator more sensitive to smaller price ranges, generating more frequent but potentially less reliable signals.
The EMA period in the trend filter helps you align your trades with the prevailing market direction. Increasing the EMA period smoothes out the trend, filtering out shorter-term fluctuations and focusing on more sustained moves. Decreasing the EMA period allows for quicker responses to changes in trend, capturing shorter-term price swings.
Potential Downsides
While the Volatility Compression Breakout indicator can provide valuable insights into potential breakouts, it's important to note that no indicator guarantees accuracy or eliminates risk. False breakouts and whipsaw movements can occur, especially in volatile or choppy market conditions. It is recommended to combine this indicator with other technical analysis tools and consider fundamental factors to validate potential trade opportunities.
Making It Work for You
To maximize the effectiveness of the Volatility Compression Breakout indicator, consider the following:
-- Combine it with other indicators : Use complementary indicators such as trend lines, oscillators, or support and resistance levels to confirm signals and increase the probability of successful trades.
-- Practice risk management : Set appropriate stop-loss levels to protect your capital in case of false breakouts or adverse price movements. Consider implementing trailing stops or adjusting stop-loss levels as the trade progresses.
-- Validate with price action : Analyze the price action within the compression phase and look for signs of building momentum or weakening trends. Support your decisions by observing candlestick patterns and volume behavior during the breakout.
-- Backtest and optimize : Test the indicator's performance across different timeframes and market conditions. Optimize the parameters based on historical data to find the most suitable settings for your trading strategy.
Remember, no single indicator can guarantee consistent profitability, and it's essential to use the Volatility Compression Breakout indicator as part of a comprehensive trading plan. Regularly review and adapt your strategy based on market conditions and your trading experience. Monitor the indicator's performance and make necessary adjustments to parameter values if the market dynamics change.
By adjusting the parameters and incorporating additional analysis techniques, you can customize the indicator to suit your trading style and preferences. However, it is crucial to exercise caution, conduct thorough analysis, and practice proper risk management to increase the likelihood of successful trades. Remember that no indicator can guarantee profits, and continuous learning and adaptation are key to long-term trading success.
Volatility Barometer (VB)Volatility Barometer (VB)
The Volatility Barometer (VB) is a comprehensive market sentiment indicator designed to measure aggregate stress and fear in the equity market. It consolidates three critical volatility metrics into a single, easy-to-interpret score, providing a broader view of market conditions than any single metric alone.
Core Components
The barometer synthesizes information from:
VIX Index (VIX): The standard measure of implied 30-day stock market volatility.
VVIX Index (VVIX): The volatility of the VIX itself, often seen as the "volatility of volatility." High VVIX readings can signal uncertainty about the VIX's future path.
VIX Futures Term Structure (VX1!−VX2!): The spread between the front-month and second-month VIX futures. A positive spread (contango) is typical, while a negative spread (backwardation) often signals imminent market stress.
How It Works
To create a unified view, the indicator normalizes each of these three components using a Z-score. The Z-score measures how many standard deviations a value is from its historical mean over a user-defined period (defaulting to 252 days, or one trading year).
These three standardized Z-scores are then combined into a final VB Score using a weighted average. Users can customize these weights in the indicator's settings to emphasize the components they find most important.
How to Interpret
The VB Score is plotted as a single line that oscillates around a zero level, with its color changing to reflect the prevailing market regime:
High Stress (Red Line): When the score rises above the "High stress threshold" (default: 1.5), it indicates heightened market fear and risk-off sentiment. This is a period of significant stress, often associated with market downturns.
Low Stress (Green Line): When the score falls below the "Low stress threshold" (default: -1.0), it signals complacency and low perceived risk in the market. Extreme low readings can sometimes precede volatility spikes.
Neutral (Blue Line): Scores between the high and low thresholds represent normal market conditions.
By providing a weighted, multi-faceted view of volatility, the Volatility Barometer helps traders and investors identify market regimes, confirm trading biases, and anticipate potential shifts in market sentiment.
Volatility Quality Index w/ Pips Filtering [Loxx]Volatility Quality Index w/ Pips Filtering is a Volatility Quality Index indicator with various smoothing types and pips filtering
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.
Included
7 different types of smoothing average
Alerts (coming in future update, stay tuned)
Signals (coming in future update, stay tuned)
Volatility Profit (VPI) & Bollinger Bands (BB) [checkm8]Hello and welcome to my 2-in-1 indicator of Volatility Profit Indicator and Bollinger Bands.
Volatility Profit Indicator is a script inspired by Jim Berg, from a 2005 article titled "The Truth About Volatility". This is a set of bands, where the upper band is based on a moving average of highs over a given period, added to the average true range of the period. The lower band is based on a moving average of lows over the same given period, from which the average true range is subtracted. The formula is shown below:
VPI High Band = MA(HI, type, x) + y * ATR(z)
VPI Low Band = MA(LO, type, x) - y * ATR(z)
where... type = MA Type (default to Exponential) x = MA Period (default to 13) y = True Range Multiplier (default to 2) z = True Range Period (default to 20)
Bollinger Bands should be familiar by now, but they are calculated based on a moving average of a source ( / 3), added to a particular standard deviation of the source.
This indicator:
- Allows you to choose whether you want to plot the Volatility Profit Indicator or Bollinger Bands. By default, each will show three bands.
- Has pre-made color schemes to choose from to simplify your life.
- Has smoothing for the Volatility Profit Indicator
- Allows you to choose the source for Bollinger Bands
- Allows you to select what multiplier of the average true range the Volatility Profit Indicator plots, as well as what standard deviations the Bollinger Bands show.
If you have any additional questions, concerns, or suggestions - feel free to reach out.
All the best & happy trading.
Volatility Percentage IndicatorThis simple indicator plot 11 lines in the chart at prices that correspond to -5%, -4%, -3%, -2%, -1%, 0%, 1%, 2%, 3%, 4%, 5%, referred to realtime price.
So the lines will move with the price.
The indicator is intended to give an at-a-glance information on price volatility by comparing the amplitude of the last candles with the percentages above.
