Volatility after Momentum/SMA crossChanged default value of average true range, to make changes in volatility more distinct.
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XBT Volatility Weighted Bottom Finder. [For Daily Charts]An update to:
Made it into and indicator.
v. 0.0.1
DESIGNED FOR DAILY CHARTS
Interval Volatility Bands [DW]This is an experimental study that utilizes Volume Weighted Average Price or Time Weighted Average Price calculations, Bollinger Bands, and Fibonacci numbers to estimate volatility over a specified interval.
First, the basis is calculated by selecting:
-VWAP, which has the option to be calculated using real volume or tick volume
-TWAP, which has the option to be calculated using the standard method or exponential method
Next, standard deviation from the basis is calculated and multiplied by a specified expansion coefficient. The result is then added to and subtracted from the basis to calculate the high and low bands.
There are three band calculation methods to chosse from in this script:
-Standard, which uses the default calculations
-Average, which takes a cumulative average of standard deviation
-Hybrid, which takes the maximum of the standard and average standard deviation methods
Lastly, the high and low band ranges are multiplied by Fibonacci Percentages 23.6 - 78.6.
A custom color scheme with eight default presets to choose from is included.
Garman Klass VolatilityThe Garman and Klass estimator for estimating historical volatility assumes Brownian motion with zero drift and no opening jumps (i.e. the opening = close of the previous period). This estimator is 7.4 times more efficient than the close-to-close estimator.
Dhananjay Volatility stop strategy v1.0
Sharing one more strategy after getting good feedback on my earlier published strategy.
This is simple volatility stop strategy where in we are using VStop as entry and exit point.
Again smart traders can add MA to decide the trend and can avoid trading in opposite direction of trend which will help them to minimize loss making trades.
There are more than 1 parameters which traders can use/change to make this strategy compatible to their instrument.
To understand what is VStop use google.com :-)
Looking forward to receiving feedback from all of you.
Periodic Volatility Channels [DW]This is an experimental study in which a geometric moving average is taken of price, then the range is multiplied by average annualized volatility based on the current trading timeframe and specified lookback, and by Fibonacci numbers 1 through 21.
Compression Bar - alphatradingdotin Volatility trading is the term used to describe trading the volatility of the price of an underlying instrument rather than the price itself. Volatility trading is simply buying and selling the expected future volatility of the instrument. There are many ways or rather innumerable ways to measure volatility one of the easiest is - a range of the price bars.
Volatility is often thought of as measuring risk or uncertainty. We are never sure where an asset is going to be at some point in the future, but a more volatile asset or underlying will have a wider spread of likely ending values, compared to a less volatile asset for the same time period.
Volatility Stats compared to BTCMeasuring ADR we can find the difference between the daily range of BTC and the daily range of altcoins in order to find something more volatile (in percentage terms) to trade.
GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.
VIX Volatility Trend Analysis With Signals - Stocks OnlyVIX VOLATILITY TREND ANALYSIS CLOUD WITH BULLISH & BEARISH SIGNALS - STOCKS ONLY
This indicator is a visual aid that shows you the bullish or bearish trend of VIX market volatility so you can see the VIX trend without switching charts. When volatility goes up, most stocks go down and vice versa. When the cloud turns green, it is a bullish sign. When the cloud turns red, it is a bearish sign.
This indicator is meant for stocks with a lot of price action and volatility, so for best results, use it on charts that move similar to the S&P 500 or other similar charts.
This indicator uses real time data from the stock market overall, so it should only be used on stocks and will only give a few signals during after hours. It does work ok for crypto, but will not give signals when the US stock market is closed.
**HOW TO USE**
When the VIX Volatility Index trend changes direction, it will give a green or red line on the chart depending on which way the VIX is now trending. The cloud will also change color depending on which way the VIX is trending. Use this to determine overall market volatility and place trades in the direction that the indicator is showing. Do not use this by itself as sometimes markets won’t react perfectly to the overall market volatility. It should only be used as a secondary confirmation in your trading/trend analysis.
For more signals with earlier entries, go into settings and reduce the number. 10-100 is best for scalping. For less signals with later entries, change the number to a higher value. Use 100-500 for swing trades. Can go higher for long swing trades. Our favorite settings are 20, 60, 100, 500 and 1000.
***MARKETS***
This indicator should only be used on the US stock markets as signals are given based on the VIX volatility index which measures volatility of the US Stock Markets.
***TIMEFRAMES***
This indicator works on all time frames, but after hours will not change much at all due to the markets being closed.
**INVERSE CHARTS**
If you are using this on an inverse ETF and the signals are showing backwards, please comment with what chart it is and I will configure the indicator to give the correct signals. I have included over 50 inverse ETFs into the code to show the correct signals on inverse charts, but I'm sure there are some that I have missed so feel free to let me know and I will update the script with the requested tickers.
***TIPS***
Try using numerous indicators of ours on your chart so you can instantly see the bullish or bearish trend of multiple indicators in real time without having to analyze the data. Some of our favorites are our Auto Fibonacci, Directional Movement Index, Volume Profile with buy & sell pressure, Auto Support And Resistance, Vix Scalper and Money Flow Index in combination with this Vix Trend Analysis. They all have real time Bullish and Bearish labels as well so you can immediately understand each indicator's trend.
Rainbow EMA Areas with Volatility HighlightThe indicator provides traders with an enhanced visual tool to observe price movements, trend strength, and market volatility on their charts. It combines multiple EMAs (Exponential Moving Averages) with color-coded areas to indicate the market’s directional bias and a high-volatility highlight for detecting times of increased market activity.
Explanation of Key Components
Multiple EMAs (Exponential Moving Averages):
Six different EMAs are calculated for various periods (15, 45, 100, 150, 200, 300).
Each EMA period represents a different timeframe, from short-term to long-term trends, providing a well-rounded view of price behavior across different market cycles.
The EMAs are color-coded for easy differentiation:
Green shades indicate bullish trends when prices are above the EMAs.
Red shades indicate bearish trends when prices are below the EMAs.
The space between each EMA is filled with a gradient color, creating a "wave" effect that helps identify the market’s overall direction.
ATR-Based Volatility Detection:
The ATR (Average True Range), a measure of market volatility, is used to assess how much the price is fluctuating. When volatility is high, price movements are typically more significant, indicating potential trading opportunities or times to exercise caution.
The indicator calculates ATR and uses a customizable multiplier to set a high-volatility threshold.
When the ATR exceeds this threshold, it signals that the market is experiencing high volatility.
Visual High Volatility Highlight:
A yellow background appears on the chart during periods of high volatility, giving a subtle but clear visual indication that the market is active.
This highlight helps traders spot potential breakout areas or increased activity zones without obstructing the EMA areas.
Volatility Signal Markers:
Small, red triangular markers are plotted above price bars when high volatility is detected, marking these areas for additional emphasis.
These signals serve as alerts to help traders quickly recognize high volatility moments where price moves may be stronger.
How to Use This Indicator
Identify Trends Using EMA Areas:
Bullish Trend: When the price is above most or all EMAs, and the EMA areas are colored in shades of green, it indicates a strong bullish trend. Traders might look for buy opportunities in this scenario.
Bearish Trend: When the price is below most or all EMAs, and the EMA areas are colored in shades of red, it signals a bearish trend. This condition can suggest potential sell opportunities.
Consolidation or Neutral Trend: If the price is moving within the EMA bands without a clear green or red dominance, the market may be in a consolidation phase. This period often precedes a breakout in either direction.
Volatility-Based Entries and Exits:
High Volatility Areas: The yellow background and red triangular markers signal high-volatility areas. This information can be valuable for identifying potential breakout points or strong moves.
Trading in High Volatility: During high-volatility phases, the market may experience rapid price changes, which can be ideal for breakout trades. However, high volatility also involves higher risk, so traders may adjust their strategies accordingly (e.g., setting wider stops or adjusting position sizes).
Trading in Low Volatility: When the yellow background and markers are absent, volatility is lower, indicating a calmer market. In these times, traders may choose to look for range-bound trading opportunities or wait for the next trend to develop.
Combining with Other Indicators:
This indicator works well in combination with momentum or oscillating indicators like RSI or MACD, providing a well-rounded view of the market.
For example, if the indicator shows a bullish EMA area with high volatility, and an RSI is trending up, it could be a stronger buy signal. Conversely, if the indicator shows a bearish EMA area with high volatility and RSI is trending down, this could be a stronger sell signal.
Practical Trading Examples
Bullish Trend in High Volatility:
Price is above the EMAs, showing green EMA areas, and the high volatility background is active.
This indicates a strong bullish trend with significant price movement potential.
A trader could look for breakout or continuation entries in the direction of the trend.
Bearish Reversal Signal:
Price crosses below the EMAs, showing red EMA areas, while high volatility is also detected.
This suggests that the market may be reversing to a bearish trend with increased price movement.
Traders could consider taking short positions or setting stops on existing long trades.
This indicator is designed to provide a rich visual experience, making it easy to spot trends, consolidations, and volatility zones at a glance. It is best used by traders who benefit from visual cues and who seek a quick understanding of both trend direction and market activity. Let me know if you'd like further customization or additional functionalities!
Z-Score Based Momentum Zones with Advanced Volatility ChannelsThe indicator "Z-Score Based Momentum Zones with Advanced Volatility Channels" combines various technical analysis components, including volatility, price changes, and volume correction, to calculate Z-Scores and determine momentum zones and provide a visual representation of price movements and volatility based on multi timeframe highest high and lowest low values.
Note: THIS IS A IMPROVEMNT OF "Multi Time Frame Composite Bands" INDICATOR OF MINE WITH MORE EMPHASIS ON MOMENTUM ZONES CALULATED BASED ON Z-SCORES
Input Options
look_back_length: This input specifies the look-back period for calculating intraday volatility. correction It is set to a default value of 5.
lookback_period: This input sets the look-back period for calculating relative price change. The default value is 5.
zscore_period: This input determines the look-back period for calculating the Z-Score. The default value is 500.
avgZscore_length: This input defines the length of the momentum block used in calculations, with a default value of 14.
include_vc: This is a boolean input that, if set to true, enables volume correction in the calculations. By default, it is set to false.
1. Volatility Bands (Composite High and Low):
Composite High and Low: These are calculated by combining different moving averages of the high prices (high) and low prices (low). Specifically:
a_high and a_low are calculated as the average of the highest (ta.highest) and lowest (ta.lowest) high and low prices over various look-back periods (5, 8, 13, 21, 34) to capture short and long-term trends.
b_high and b_low are calculated as the simple moving average (SMA) of the high and low prices over different look-back periods (5, 8, 13) to smooth out the trends.
high_c and low_c are obtained by averaging a_high with b_high and a_low with b_low respectively.
IDV Correction Calulation : In this script the Intraday Volatility (IDV) is calculated as the simple moving average (SMA) of the daily high-low price range divided by the closing price. This measures how much the price fluctuates in a given period.
Composite High and Low with Volatility: The final c_high and c_low values are obtained by adjusting high_c and low_c with the calculated intraday volatility (IDV). These values are used to create the "Composite High" and "Composite Low" plots.
Composite High and Low with Volatility Correction: The final c_high and c_low values are obtained by adjusting high_c and low_c with the calculated intraday volatility (IDV). These values are used to create the "Composite High" and "Composite Low" plots.
2. Momentum Blocks Based on Z-Score:
Relative Price Change (RPC):
The Relative Price Change (rpdev) is calculated as the difference between the current high-low-close average (hlc3) and the previous simple moving average (psma_hlc3) of the same quantity. This measures the change in price over time.
Additionally, std_hlc3 is calculated as the standard deviation of the hlc3 values over a specified look-back period. The standard deviation quantifies the dispersion or volatility in the price data.
The rpdev is then divided by the std_hlc3 to normalize the price change by the volatility. This normalization ensures that the price change is expressed in terms of standard deviations, which is a common practice in quantitative analysis.
Essentially, the rpdev represents how many standard deviations the current price is away from the previous moving average.
Volume Correction (VC): If the include_vc input is set to true, volume correction is applied by dividing the trading volume by the previous simple moving average of the volume (psma_volume). This accounts for changes in trading activity.
Volume Corrected Relative Price Change (VCRPD): The vcrpd is calculated by multiplying the rpdev by the volume correction factor (vc). This incorporates both price changes and volume data.
Z-Scores: The Z-scores are calculated by taking the difference between the vcrpd and the mean (mean_vcrpd) and then dividing it by the standard deviation (stddev_vcrpd). Z-scores measure how many standard deviations a value is away from the mean. They help identify whether a value is unusually high or low compared to its historical distribution.
Momentum Blocks: The "Momentum Blocks" are essentially derived from the Z-scores (avgZScore). The script assigns different colors to the "Fill Area" based on predefined Z-score ranges. These colored areas represent different momentum zones:
Positive Z-scores indicate bullish momentum, and different shades of green are used to fill the area.
Negative Z-scores indicate bearish momentum, and different shades of red are used.
Z-scores near zero (between -0.25 and 0.25) suggest neutrality, and a yellow color is used.
TechniTrend: Volatility and MACD Trend Highlighter🟦 Overview
The "Candle Volatility with Trend Prediction" indicator is a powerful tool designed to identify market volatility based on candle movement relative to average volume while also incorporating trend predictions using the MACD. This indicator is ideal for traders who want to detect volatile market conditions and anticipate potential price movements, leveraging both price changes and volume dynamics.
It not only highlights candles with significant price movements but also integrates a trend analysis based on the MACD (Moving Average Convergence Divergence), allowing traders to gauge whether the market momentum aligns with or diverges from the detected volatility.
🟦 Key Features
🔸Volatility Detection: Identifies candles that exceed normal price fluctuations based on average volume and recent price volatility.
🔸Trend Prediction: Uses the MACD indicator to overlay trend analysis, signaling potential market direction shifts.
🔸Volume-Based Analysis: Integrates customizable moving averages (SMA, EMA, WMA, etc.) of volume, providing a clear visualization of volume trends.
🔸Alert System: Automatically notifies traders of high-volatility situations, aiding in timely decision-making.
🔸Customizability: Includes multiple settings to tailor the indicator to different market conditions and timeframes.
🟦 How It Works
The indicator operates by evaluating the price volatility in relation to average volume and identifying when a candle's volatility surpasses a threshold defined by the user. The key calculations include:
🔸Average Volume Calculation: The user selects the type of moving average (SMA, EMA, etc.) to calculate the average volume over a set period.
🔸Volatility Measurement: The indicator measures the body change (difference between open and close) and the high-low range of each candle. It then calculates recent price volatility using a standard deviation over a user-defined length.
🔸Weighted Index: A unique index is created by dividing price change by average volume and recent volatility.
🔸Highlighting Volatility: If the weighted index exceeds a customizable threshold, the candle is highlighted, indicating potential trading opportunities.
🔸Trend Analysis with MACD: The MACD line and signal line are plotted and adjusted with a user-defined multiplier to visualize trends alongside the volatility signals.
🟦 Recommended Settings
🔸Volume MA Length: A default of 14 periods for the average volume calculation is recommended. Adjust to higher periods for long-term trends and shorter periods for quick trades.
🔸Volatility Threshold Multiplier: Set at 1.2 by default to capture moderately significant movements. Increase for fewer but stronger signals or decrease for more frequent signals.
🔸MACD Settings: Default MACD parameters (12, 26, 9) are suggested. Tweak based on your trading strategy and asset volatility.
🔸MACD Multiplier: Adjust based on how the MACD should visually compare to the average volume. A multiplier of 1 works well for most cases.
🟦 How to Use
🔸Volatile Market Detection:
Look for highlighted candles that suggest a deviation from typical price behavior. These candles often signify an entry point for short-term trades.
🔸Trend Confirmation:
Use the MACD trend analysis to verify if the highlighted volatile candles align with a bullish or bearish trend.
For example, a bullish MACD crossover combined with a highlighted candle suggests a potential uptrend, while a bearish crossover with volatility signals may indicate a downtrend.
🔸Volume-Driven Strategy:
Observe how volume changes impact candle volatility. When volume rises significantly and candles are highlighted, it can suggest strong market moves influenced by big players.
🟦 Best Use Cases
🔸Trend Reversals: Detect potential trend reversals early by spotting divergences between price and MACD within volatile conditions.
🔸Breakout Strategies: Use the indicator to confirm price breakouts with significant volume changes.
🔸Scalping or Day Trading: Customize the indicator for shorter timeframes to capture rapid market movements based on volatility spikes.
🔸Swing Trading: Combine volatility and trend insights to optimize entry and exit points over longer periods.
🟦 Customization Options
🔸Volume-Based Inputs: Choose from SMA, EMA, WMA, and more to define how average volume is calculated.
🔸Threshold Adjustments: Modify the volatility threshold multiplier to increase or decrease sensitivity based on your trading style.
🔸MACD Tuning: Adjust MACD settings and the multiplier for trend visualization tailored to different asset classes and market conditions.
🟦 Indicator Alerts
🔸High Volatility Alerts: Automatically triggered when candles exceed user-defined volatility levels.
🔸Bullish/Bearish Trend Alerts: Alerts are activated when highlighted volatile candles align with bullish or bearish MACD crossovers, making it easier to spot opportunities without constantly monitoring the chart.
🟦 Examples of Use
To better understand how this indicator works, consider the following scenarios:
🔸Example 1: In a strong uptrend, observe how volume surges and volatility highlight candles right before price consolidations, indicating optimal exit points.
🔸Example 2: During a downtrend, see how the MACD aligns with volume-driven volatility, signaling potential short-selling opportunities.
SOLANA Performance & Volatility Analysis BB%Overview:
The script provides an in-depth analysis of Solana's performance and volatility. It showcases Solana's price, its inverse relationship, its own volatility, and even juxtaposes it against Bitcoin's 24-hour historical volatility. All of these are presented using the Bollinger Bands Percentage (BB%) methodology to normalise the price and volatility values between 0 and 1.
Key Components:
Inputs:
SOLANA PRICE (SOLUSD): The price of Solana.
SOLANA INVERSE (SOLUSDT.3S): The inverse of Solana's price.
SOLANA VOLATILITY (SOLUSDSHORTS): Volatility for Solana.
BITCOIN 24 HOUR HISTORICAL VOLATILITY (BVOL24H): Bitcoin's volatility over the past 24 hours.
BB Calculations:
The script uses the Bollinger Bands methodology to calculate the mean (SMA) and the standard deviation of the prices and volatilities over a certain period (default is 20 periods). The calculated upper and lower bands help in normalising the values to the range of 0 to 1.
Normalised Metrics Plotting:
For better visualisation and comparative analysis, the normalised values for:
Solana Price
Solana Inverse
Solana Volatility
Bitcoin 24hr Volatility
are plotted with steplines.
Band Plotting:
Bands are plotted at 20%, 40%, 60%, and 80% levels to serve as reference points. The area between the 40% and 60% bands is shaded to highlight the median region.
Colour Coding:
Different colours are used for easy differentiation:
Solana Price: Blue
Solana Inverse: Red
Solana Volatility: Green
Bitcoin 24hr Volatility: White
Licence & Creator:
The script adheres to the Mozilla Public Licence 2.0 and is credited to the author, "Volatility_Vibes".
Works well with Breaks and Retests with Volatility Stop
Bandwidth Volatility - Silverman Rule of thumb EstimatorOverview
This indicator calculates volatility using the Rule of Thumb bandwidth estimator and incorporating the standard deviations of returns to get historical volatility. There are two options: one for the original rule of thumb bandwidth estimator, and another for the modified rule of thumb estimator. This indicator comes with the bandwidth , which is shown with the color gradient columns, which are colored by a percentile of the bandwidth, and the moving average of the bandwidth, which is the dark shaded area.
The rule of thumb bandwidth estimator is a simple and quick method for estimating the bandwidth parameter in kernel density estimation (KSE) or kernel regression. It provides a rough approximation of the bandwidth without requiring extensive computation resources or fine-tuning. One common rule of thumb estimator is Silverman rule, which is given by
h = 1.06*σ*n^(-1/5)
where
h is the bandwidth
σ is the standard deviation of the data
n is the number of data points
This rule of thumb is based on assuming a Gaussian kernel and aims to strike a balance between over-smoothing and under-smoothing the data. It is simple to implement and usually provides reasonable bandwidth estimates for a wide range of datasets. However , it is important to note that this rule of thumb may not always have optimal results, especially for non-Gaussian or multimodal distributions. In such cases, a modified bandwidth selection, such as cross-validation or even applying a log transformation (if the data is right-skewed), may be preferable.
How it works:
This indicator computes the bandwidth volatility using returns, which are used in the standard deviation calculation. It then estimates the bandwidth based on either the Silverman rule of thumb or a modified version considering the interquartile range. The percentile ranks of the bandwidth estimate are then used to visualize the volatility levels, identify high and low volatility periods, and show them with colors.
Modified Rule of thumb Bandwidth:
The modified rule of thumb bandwidth formula combines elements of standard deviations and interquartile ranges, scaled by a multiplier of 0.9 and inversely with a number of periods. This modification aims to provide a more robust and adaptable bandwidth estimation method, particularly suitable for financial time series data with potentially skewed or heavy-tailed data.
Formula for Modified Rule of Thumb Bandwidth:
h = 0.9 * min(σ, (IQR/1.34))*n^(-1/5)
This modification introduces the use of the IQR divided by 1.34 as an alternative to the standard deviation. It aims to improve the estimation, mainly when the underlying distribution deviates from a perfect Gaussian distribution.
Analysis
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Modelling Requirements
Rule of thumb Bandwidth: Provides a broader perspective on volatility trends, smoothing out short-term fluctuations and focusing more on the overall shape of the density function.
Historical Volatility: Offers a more granular view of volatility, capturing day-to-day or intra-period fluctuations in asset prices and returns.
Pros of Bandwidth as a volatility measure
Robust to Data Distribution: Bandwidth volatility, especially when estimated using robust methods like Silverman's rule of thumb or its modifications, can be less sensitive to outliers and non-normal distributions compared to some other measures of volatility
Flexibility: It can be applied to a wide range of data types and can adapt to different underlying data distributions, making it versatile for various analytical tasks.
How can traders use this indicator?
In finance, volatility is thought to be a mean-reverting process. So when volatility is at an extreme low, it is expected that a volatility expansion happens, which comes with bigger movements in price, and when volatility is at an extreme high, it is expected for volatility to eventually decrease, leading to smaller price moves, and many traders view this as an area to take profit in.
In the context of this indicator, low volatility is thought of as having the green color, which indicates a low percentile value, and also being below the moving average. High volatility is thought of as having the yellow color and possibly being above the moving average, showing that you can eventually expect volatility to decrease.
Grid by Volatility (Expo)█ Overview
The Grid by Volatility is designed to provide a dynamic grid overlay on your price chart. This grid is calculated based on the volatility and adjusts in real-time as market conditions change. The indicator uses Standard Deviation to determine volatility and is useful for traders looking to understand price volatility patterns, determine potential support and resistance levels, or validate other trading signals.
█ How It Works
The indicator initiates its computations by assessing the market volatility through an established statistical model: the Standard Deviation. Following the volatility determination, the algorithm calculates a central equilibrium line—commonly referred to as the "mid-line"—on the chart to serve as a baseline for additional computations. Subsequently, upper and lower grid lines are algorithmically generated and plotted equidistantly from the central mid-line, with the distance being dictated by the previously calculated volatility metrics.
█ How to Use
Trend Analysis: The grid can be used to analyze the underlying trend of the asset. For example, if the price is above the Average Line and moves toward the Upper Range, it indicates a strong bullish trend.
Support and Resistance: The grid lines can act as dynamic support and resistance levels. Price tends to bounce off these levels or breakthrough, providing potential trade opportunities.
Volatility Gauge: The distance between the grid lines serves as a measure of market volatility. Wider lines indicate higher volatility, while narrower lines suggest low volatility.
█ Settings
Volatility Length: Number of bars to calculate the Standard Deviation (Default: 200)
Squeeze Adjustment: Multiplier for the Standard Deviation (Default: 6)
Grid Confirmation Length: Number of bars to calculate the weighted moving average for smoothing the grid lines (Default: 2)
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Relative Volatility Mass [SciQua]The ⚖️ Relative Volatility Mass (RVM) is a volatility-based tool inspired by the Relative Volatility Index (RVI) .
While the RVI measures the ratio of upward to downward volatility over a period, RVM takes a different approach:
It sums the standard deviation of price changes over a rolling window, separating upward volatility from downward volatility .
The result is a measure of the total “volatility mass” over a user-defined period, rather than an average or normalized ratio.
This makes RVM particularly useful for identifying sustained high-volatility conditions without being diluted by averaging.
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How It Works
╰────────────╯
1. Standard Deviation Calculation
• Computes the standard deviation of the chosen `Source` over a `Standard Deviation Length` (`stdDevLen`).
2. Directional Separation
• Volatility on up bars (`chg > 0`) is treated as upward volatility .
• Volatility on down bars (`chg < 0`) is treated as downward volatility .
3. Rolling Sum
• Over a `Sum Length` (`sumLen`), the upward and downward volatilities are summed separately using `math.sum()`.
4. Relative Volatility Mass
• The two sums are added together to get the total volatility mass for the rolling window.
Formula:
RVM = Σ(σ up) + Σ(σ down)
where σ is the standard deviation over `stdDevLen`.
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Key Features
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Directional Volatility Tracking – Differentiates between volatility during price advances vs. declines.
Rolling Volatility Mass – Shows the total standard deviation accumulation over a given period.
Optional Smoothing – Multiple MA types, including SMA, EMA, SMMA (RMA), WMA, VWMA.
Bollinger Band Overlay – Available when SMA is selected, with adjustable standard deviation multiplier.
Configurable Source – Apply RVM to `close`, `open`, `hl2`, or any custom source.
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Usage
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Trend Confirmation: High RVM values can confirm strong trending conditions.
Breakout Detection: Spikes in RVM often precede or accompany price breakouts.
Volatility Cycle Analysis: Compare periods of contraction and expansion.
RVM is not bounded like the RVI, so absolute values depend on market volatility and chosen parameters.
Consider normalizing or using smoothing for easier visual comparison.
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Example Settings
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Short-term volatility detection: `stdDevLen = 5`, `sumLen = 10`
Medium-term trend volatility: `stdDevLen = 14`, `sumLen = 20`
Enable `SMA + Bollinger Bands` to visualize when volatility is unusually high or low relative to recent history.
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Notes & Limitations
╰───────────────────╯
Not a directional signal by itself — use alongside price structure, volume, or other indicators.
Higher `sumLen` will smooth short-term fluctuations but reduce responsiveness.
Because it sums, not averages, values will scale with both volatility and chosen window size.
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Credits
╰───────╯
Based on the Relative Volatility Index concept by Donald Dorsey (1993).
TradingView
SciQua - Joshua Danford
Realized volatility differentialAbout
This is a simple indicator that takes into account two types of realized volatility: Close-Close and High-Low (the latter is more useful for intraday trading).
The output of the indicator is two values / plots:
an average of High-Low volatility minus Close-Close volatility (10day period is used as a default)
the current value of the indicator
When the current value is:
lower / below the average, then it means that High-Low volatility should increase.
higher / above then obviously the opposite is true.
How to use it
It might be used as a timing tool for mean reversion strategies = when your primary strategy says a market is in mean reversion mode, you could use it as a signal for opening a position.
For example: let's say a security is in uptrend and approaching an important level (important to you).
If the current value is:
above the average, a short position can be opened, as High-Low volatility should decrease;
below the average, a trend should continue.
Intended securities
Futures contracts
