Linear Regression with StdDev BandsLinear Regression with Standard Deviation Bands Indicator
This indicator plots a linear regression line along with upper and lower bands based on standard deviation. It helps identify potential overbought and oversold conditions, as well as trend direction and strength.
Key Components:
Linear Regression Line: Represents the average price over a specified period.
Upper and Lower Bands: Calculated by adding and subtracting the standard deviation (multiplied by a user-defined factor) from the linear regression line. These bands act as dynamic support and resistance levels.
How to Use:
Trend Identification: The direction of the linear regression line indicates the prevailing trend.
Overbought/Oversold Signals: Prices approaching or crossing the upper band may suggest overbought conditions, while prices near the lower band may indicate oversold conditions.
Dynamic Support/Resistance: The bands can act as potential support and resistance levels.
Alerts: Option to enable alerts when the price crosses above the upper band or below the lower band.
Customization:
Regression Length: Adjust the period over which the linear regression is calculated.
StdDev Multiplier: Modify the width of the bands by changing the standard deviation multiplier.
Price Source: Choose which price data to use for calculations (e.g., close, open, high, low).
Alerts: Enable or disable alerts for band crossings.
This indicator is a versatile tool for understanding price trends and potential reversal points.
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MACD & Bollinger Bands Overbought OversoldMACD & Bollinger Bands Reversal Detector
This indicator combines the power of MACD divergence analysis with Bollinger Bands to help traders identify potential reversal points in the market.
Key Features:
MACD Calculation & Divergence:
The script calculates the standard MACD components (MACD line, Signal line, and Histogram) using configurable fast, slow, and signal lengths. It includes a simplified divergence detection mechanism that flags potential bearish divergence—when the price makes a new swing high but the MACD fails to confirm the move. This divergence can serve as an early warning that the bullish momentum is waning.
Bollinger Bands:
A 20-period simple moving average (SMA) is used as the basis, with upper and lower bands drawn at 2 standard deviations. These bands help visualize overbought and oversold conditions. For example, a close at or above the upper band suggests the market may be overextended (overbought), while a close at or below the lower band may indicate oversold conditions.
Visual Alerts:
The indicator plots the Bollinger Bands on the chart along with labels marking overbought and oversold conditions. Additionally, it marks potential bearish divergence with a downward triangle, providing a quick visual cue to traders.
Usage Suggestions:
Confluence with Other Signals:
Use the divergence signals and Bollinger Band conditions as filters. For example, even if another indicator suggests a long entry, you might avoid it if the price is overbought or if MACD divergence warns of weakening momentum.
Customization:
All key parameters, such as the MACD lengths, Bollinger Band period, and multiplier, are fully configurable. This flexibility allows you to adjust the indicator to suit different markets or trading styles.
Disclaimer:
This script is provided for educational purposes only. Always perform your own analysis and backtesting before trading with live capital.
Multiple Bollinger Bands + Volatility [AlgoTraderPro]This indicator helps traders visualize price ranges and volatility changes. Designed to assist in identifying potential consolidation zones, the indicator uses multiple layers of Bollinger Bands combined with volatility-based shading. This can help traders spot periods of reduced price movement, which are often followed by breakouts or trend reversals.
█ FEATURES
Multiple Bollinger Bands: Displays up to seven bands with customizable standard deviations, providing a layered view of price range activity.
Volatility Measurement: Tracks changes in Bollinger Band width to display volatility percentage and direction (increasing, decreasing, or neutral).
Volatility Shading: Uses color-coded shading between the outermost bands to indicate changes in volatility, helping to visualize potential consolidation zones.
Customizable Inputs: Modify lookback periods, moving average lengths, and standard deviations for each band to tailor the analysis to your strategy.
Volatility Table: Displays a table on the chart showing real-time volatility data and direction for quick reference.
█ HOW TO USE
Add the Indicator: Apply it to your TradingView chart.
Adjust Settings: Customize the Bollinger Bands’ parameters to suit your trading timeframe and strategy.
Analyze Consolidation Zones: Use the multiple bands and volatility shading to identify areas of reduced price activity, signaling potential breakouts.
Monitor Volatility: Refer to the volatility table to track real-time shifts in market volatility.
Use in Different Markets: Adapt the settings for various assets and timeframes to assess market conditions effectively.
█ NOTES
• The indicator is useful in consolidating markets where price movement is limited, offering insights into potential breakout areas.
• Adjust the settings based on asset and market conditions for optimal results.
[blackcat] L3 Fibonacci Bands With ATRToday, what I'm going to introduce is a technical indicator that I think is quite in line with the indicator displayed by Tang - Fibonacci Bands with ATR. This indicator combines Bollinger Bands and Average True Range (ATR) to provide insights into market volatility and potential price reversals. Sounds complicated, right? Don't worry, I will explain it to you in the simplest way.
First, let's take a look at how Fibonacci Bands are constructed. They are similar to Bollinger Bands and consist of three lines: upper band, middle band (usually a 20-period simple moving average), and lower band. The difference is that Fibonacci Bands use ATR to calculate the distance between the upper and lower bands and the middle band.
Next is a key factor - ATR multiplier. We need to smooth the ATR using Welles Wilder's method. Then, by multiplying the ATR by a Fibonacci multiplier (e.g., 1.618), we get the upper band, called the upper Fibonacci channel. Similarly, multiplying the ATR by another Fibonacci multiplier (e.g., 0.618 or 1.0) gives us the lower band, called the lower Fibonacci channel.
Now, let's see how Fibonacci Bands can help us assess market volatility. When the channel widens, it means that market volatility is high, while a narrow channel indicates low market volatility. This way, we can determine the market's activity level based on the width of the channel.
In addition, when the price touches or crosses the Fibonacci channel, it may indicate a potential price reversal, similar to Bollinger Bands. Therefore, using Fibonacci Bands in trading can help us capture potential buy or sell signals.
In summary, Fibonacci Bands with ATR is an interesting and practical technical indicator that provides information about market volatility and potential price reversals by combining Bollinger Bands and ATR. Remember, make good use of these indicators and apply them flexibly in trading!
This code is a TradingView indicator script used to plot L3 Fibonacci Bands With ATR.
First, the indicator function is used to define the title and short title of the indicator, and whether it should be overlaid on the main chart.
Then, the input function is used to define three input parameters: MA type (maType), MA length (maLength), and data source (src). There are four options for MA type: SMA, EMA, WMA, and HMA. The default values are SMA, 55, and hl2 respectively.
Next, the moving average line is calculated based on the user's selected MA type. If maType is 'SMA', the ta.sma function is called to calculate the simple moving average; if maType is 'EMA', the ta.ema function is called to calculate the exponential moving average; if maType is 'WMA', the ta.wma function is called to calculate the weighted moving average; if maType is 'HMA', the ta.hma function is called to calculate the Hull moving average. The result is then assigned to the variable ma.
Then, the _atr variable is used to calculate the ATR (Average True Range) value using ta.atr, and multiplied by different coefficients to obtain four Fibonacci bias values: fibo_bias4, fibo_bias3, fibo_bias2, and fibo_bias1.
Finally, the prices of the upper and lower four Fibonacci bands are calculated by adding or subtracting the corresponding Fibonacci bias values from the current price, and plotted on the chart using the plot function.
VWAP Bollinger BandsWhat makes this different from vwap bands / bollinger bands?
This indicator takes a bit of inspiration from bollinger but instead of utilizing built in pine script std dev that uses simple moving average internally, this version replaces that with vwap.
Also instead of traditional bollinger band basis of 20 period simple moving average, the basis here for the bands is the vwap.
How to use it?
Usage is similar to vwap itself, though the standard deviation bands will expand and contract like normal bollinger bands instead of vwap bands that just widen as the market movement continues. The bands tell a slightly different story from bollinger bands as the underlying data utilized is the vwap itself.
Which markets is this meant for?
Any market.
What conditions?
This aids in finding conditions of entry standard to vwap, but the bands could give key areas of focus for entries and exits better than standard bollinger bands or vwap bands.
CFB Adaptive MOGALEF Bands [Loxx]A Pine Script adaptation from MOGALEF Bands .
What are MOGALEF Bands?
Actual MOGALEF bands code is the final result of a lot of contributors. Syllables MO-GA-LEF are the initials of three of them.
The basic idea of bands: the markets are still in range, and trends that are moving ranges. The Mogalef bands try to estimate the current range and to project its on the future if prices move. This future estimation is often of great relevance and very useful, especialy for market profile users or pivot points users.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Included:
-Color bars
-Fill levels
Bitcoin Power Law Bands (BTC Power Law) Indicator█ OVERVIEW
The 'Bitcoin Power Law Bands' indicator is a set of three US dollar price trendlines and two price bands for bitcoin , indicating overall long-term trend, support and resistance levels as well as oversold and overbought conditions. The magnitude and growth of the middle (Center) line is determined by double logarithmic (log-log) regression on the entire USD price history of bitcoin . The upper (Resistance) and lower (Support) lines follow the same trajectory but multiplied by respective (fixed) factors. These two lines indicate levels where the price of bitcoin is expected to meet strong long-term resistance or receive strong long-term support. The two bands between the three lines are price levels where bitcoin may be considered overbought or oversold.
All parameters and visuals may be customized by the user as needed.
█ CONCEPTS
Long-term models
Long-term price models have many challenges, the most significant of which is getting the growth curve right overall. No one can predict how a certain market, asset class, or financial instrument will unfold over several decades. In the case of bitcoin , price history is very limited and extremely volatile, and this further complicates the situation. Fortunately for us, a few smart people already had some bright ideas that seem to have stood the test of time.
Power law
The so-called power law is the only long-term bitcoin price model that has a chance of survival for the years ahead. The idea behind the power law is very simple: over time, the rapid (exponential) initial growth cannot possibly be sustained (see The seduction of the exponential curve for a fun take on this). Year-on-year returns, therefore, must decrease over time, which leads us to the concept of diminishing returns and the power law. In this context, the power law translates to linear growth on a chart with both its axes scaled logarithmically. This is called the log-log chart (as opposed to the semilog chart you see above, on which only one of the axes - price - is logarithmic).
Log-log regression
When both price and time are scaled logarithmically, the power law leads to a linear relationship between them. This in turn allows us to apply linear regression techniques, which will find the best-fitting straight line to the data points in question. The result of performing this log-log regression (i.e. linear regression on a log-log scaled dataset) is two parameters: slope (m) and intercept (b). These parameters fully describe the relationship between price and time as follows: log(P) = m * log(T) + b, where P is price and T is time. Price is measured in US dollars , and Time is counted as the number of days elapsed since bitcoin 's genesis block.
DPC model
The final piece of our puzzle is the Dynamic Power Cycle (DPC) price model of bitcoin . DPC is a long-term cyclic model that uses the power law as its foundation, to which a periodic component stemming from the block subsidy halving cycle is applied dynamically. The regression parameters of this model are re-calculated daily to ensure longevity. For the 'Bitcoin Power Law Bands' indicator, the slope and intercept parameters were calculated on publication date (March 6, 2022). The slope of the Resistance Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Nov 2021 cycle peak. The slope of the Support Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Dec 2018 trough of the previous cycle. Please see the Limitations section below on the implications of a static model.
█ FEATURES
Inputs
• Parameters
• Center Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the grey line in the middle
• Resistance Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the red line at the top
• Support Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the green line at the bottom
• Controls
• Plot Line Fill: N/A
• Plot Opportunity Label: Controls the display of current price level relative to the Center, Resistance and Support Lines
Style
• Visuals
• Center: Control, color, opacity, thickness, price line control and line style of the Center Line
• Resistance: Control, color, opacity, thickness, price line control and line style of the Resistance Line
• Support: Control, color, opacity, thickness, price line control and line style of the Support Line
• Plots Background: Control, color and opacity of the Upper Band
• Plots Background: Control, color and opacity of the Lower Band
• Labels: N/A
• Output
• Labels on price scale: Controls the display of current Center, Resistance and Support Line values on the price scale
• Values in status line: Controls the display of current Center, Resistance and Support Line values in the indicator's status line
█ HOW TO USE
The indicator includes three price lines:
• The grey Center Line in the middle shows the overall long-term bitcoin USD price trend
• The red Resistance Line at the top is an indication of where the bitcoin USD price is expected to meet strong long-term resistance
• The green Support Line at the bottom is an indication of where the bitcoin USD price is expected to receive strong long-term support
These lines envelope two price bands:
• The red Upper Band between the Center and Resistance Lines is an area where bitcoin is considered overbought (i.e. too expensive)
• The green Lower Band between the Support and Center Lines is an area where bitcoin is considered oversold (i.e. too cheap)
The power law model assumes that the price of bitcoin will fluctuate around the Center Line, by meeting resistance at the Resistance Line and finding support at the Support Line. When the current price is well below the Center Line (i.e. well into the green Lower Band), bitcoin is considered too cheap (oversold). When the current price is well above the Center Line (i.e. well into the red Upper Band), bitcoin is considered too expensive (overbought). This idea alone is not sufficient for profitable trading, but, when combined with other factors, it could guide the user's decision-making process in the right direction.
█ LIMITATIONS
The indicator is based on a static model, and for this reason it will gradually lose its usefulness. The Center Line is the most durable of the three lines since the long-term growth trend of bitcoin seems to deviate little from the power law. However, how far price extends above and below this line will change with every halving cycle (as can be seen for past cycles). Periodic updates will be needed to keep the indicator relevant. The user is invited to adjust the slope and intercept parameters manually between two updates of the indicator.
█ RAMBLINGS
The 'Bitcoin Power Law Bands' indicator is a useful tool for users wishing to place bitcoin in a macro context. As described above, the price level relative to the three lines is a rough indication of whether bitcoin is over- or undervalued. Users wishing to gain more insight into bitcoin price trends may follow the author's periodic updates of the DPC model (contact information below).
█ NOTES
The author regularly posts on Twitter using the @DeFi_initiate handle.
█ THANKS
Many thanks to the following individuals, who - one way or another - made the 'Bitcoin Power Law Bands' indicator possible:
• TradingView user 'capriole_charles', whose open-source 'Bitcoin Power Law Corridor' script was the basis for this indicator
• Harold Christopher Burger, whose Bitcoin’s natural long-term power-law corridor of growth article (2019) was the basis for the 'Bitcoin Power Law Corridor' script
• Bitcoin Forum user "Trololo", who posted the original power law model at Logarithmic (non-linear) regression - Bitcoin estimated value (2014)
No Nonsense Forex Moving Averages ATR Bands[T1][T69]🔍 Overview
This indicator implements a No Nonsense Forex-style Baseline combined with ATR Bands, built using the moving_averages_library by Teyo69. It plots a configurable moving average and dynamically adjusts upper/lower ATR bands for trade zone detection and baseline confirmation.
✨ Features
30+ Moving Average types
ATR bands to define dynamic trade zones
Visual background highlighting for trade signals
Supports both "Within Range" and "Baseline Bias" display modes
Clean, minimal overlay with effective zone coloring
⚙️ How to Use
Choose MA Type: Select the baseline logic (SMA, EMA, HMA, etc.)
Configure ATR Bands: Adjust the ATR length and multiplier
Select Background Mode:
Within Range: Yellow = price inside band, Gray = outside
Long/Short Baseline Signal: Green = price above baseline, Red = below
Trade Setup:
Use the baseline for trend direction
Wait for confirmation or avoidance when price is outside the band
🛠 Configuration
Source: Price source for MA
MA Type: Any supported MA from the library
MA Length: Number of bars for smoothing
ATR Length: Period for Average True Range
ATR Multiplier: Width of the bands
Background Signal Mode: Choose visual signal type
⚠️ Limitations
Works with one MA at a time
Requires the moving_averages_library imported
Does not include confirmation or exit logic — use with full NNFX stack
💡 Tips
Combine with Volume or Confirmation indicators for NNFX strategy
Use adaptive MAs like KAMA, JMA, or VIDYA for dynamic baselines
Adjust ATR settings based on asset volatility
📘 Credits
Library: Teyo69/moving_averages_library/1
Inspired by: No Nonsense Forex (VP) Baseline + ATR Band methodology & MigthyZinger
Faytterro Bands Breakout📌 Faytterro Bands Breakout 📌
This indicator was created as a strategy showcase for another script: Faytterro Bands
It’s meant to demonstrate a simple breakout strategy based on Faytterro Bands logic and includes performance tracking.
❓ What Is It?
This script is a visual breakout strategy based on a custom moving average and dynamic deviation bands, similar in concept to Bollinger Bands but with unique smoothing (centered regression) and performance features.
🔍 What Does It Do?
Detects breakouts above or below the Faytterro Band.
Plots visual trade entries and exits.
Labels each trade with percentage return.
Draws profit/loss lines for every trade.
Shows cumulative performance (compounded return).
Displays key metrics in the top-right corner:
Total Return
Win Rate
Total Trades
Number of Wins / Losses
🛠 How Does It Work?
Bullish Breakout: When price crosses above the upper band and stays above the midline.
Bearish Breakout: When price crosses below the lower band and stays below the midline.
Each trade is held until breakout invalidation, not a fixed TP/SL.
Trades are compounded, i.e., profits stack up realistically over time.
📈 Best Use Cases:
For traders who want to experiment with breakout strategies.
For visual learners who want to study past breakouts with performance metrics.
As a template to develop your own logic on top of Faytterro Bands.
⚠ Notes:
This is a strategy-like visual indicator, not an automated backtest.
It doesn't use strategy.* commands, so you can still use alerts and visuals.
You can tweak the logic to create your own backtest-ready strategy.
Unlike the original Faytterro Bands, this script does not repaint and is fully stable on closed candles.
Mad Trading Scientist - Guppy MMA with Bollinger Bands📘 Indicator Name:
Guppy MMA with Bollinger Bands
🔍 What This Indicator Does:
This TradingView indicator combines Guppy Multiple Moving Averages (GMMA) with Bollinger Bands to help you identify trend direction and volatility zones, ideal for spotting pullback entries within trending markets.
🔵 1. Guppy Multiple Moving Averages (GMMA):
✅ Short-Term EMAs (Blue) — represent trader sentiment:
EMA 3, 5, 8, 10, 12, 15
✅ Long-Term EMAs (Red) — represent investor sentiment:
EMA 30, 35, 40, 45, 50, 60
Usage:
When blue (short) EMAs are above red (long) EMAs and spreading → Strong uptrend
When blue EMAs cross below red EMAs → Potential downtrend
⚫ 2. Bollinger Bands (Volatility Envelopes):
Length: 300 (captures the longer-term price range)
Basis: 300-period SMA
Upper & Lower Bands:
±1 Standard Deviation (light gray zone)
±2 Standard Deviations (dark gray zone)
Fill Zones:
Highlights standard deviation ranges
Emphasizes extreme vs. normal price moves
Usage:
Price touching ±2 SD bands signals potential exhaustion
Price reverting to the mean suggests pullback or re-entry opportunity
💡 Important Note: Use With Momentum Filter
✅ For superior accuracy, this indicator should be combined with your invite-only momentum filter on TradingView.
This filter helps confirm whether the trend has underlying strength or is losing momentum, increasing the probability of successful entries and exits.
🕒 Recommended Timeframe:
📆 1-Hour Chart (60m)
This setup is optimized for short- to medium-term swing trading, where Guppy structures and Bollinger reversion work best.
🔧 Practical Strategy Example:
Long Trade Setup:
Short EMAs are above long EMAs (strong uptrend)
Price pulls back to the lower 1 or 2 SD band
Momentum filter confirms bullish strength
Short Trade Setup:
Short EMAs are below long EMAs (strong downtrend)
Price rises to the upper 1 or 2 SD band
Momentum filter confirms bearish strength
Quantitative Breakout Bands (AIBitcoinTrend)Quantitative Breakout Bands (AIBitcoinTrend) is an advanced indicator designed to adapt to dynamic market conditions by utilizing a Kalman filter for real-time data analysis and trend detection. This innovative tool empowers traders to identify price breakouts, evaluate trends, and refine their trading strategies with precision.
👽 What Are Quantitative Breakout Bands, and Why Are They Unique?
Quantitative Breakout Bands combine advanced filtering techniques (Kalman Filters) with statistical measures such as mean absolute error (MAE) to create adaptive price bands. These bands adjust to market conditions dynamically, providing insights into volatility, trend strength, and breakout opportunities.
What sets this indicator apart is its ability to incorporate both position (price) and velocity (rate of price change) into its calculations, making it highly responsive yet smooth. This dual consideration ensures traders get reliable signals without excessive lag or noise.
👽 The Math Behind the Indicator
👾 Kalman Filter Estimation:
At the core of the indicator is the Kalman Filter, a recursive algorithm used to predict the next state of a system based on past observations. It incorporates two primary elements:
State Prediction: The indicator predicts future price (position) and velocity based on previous values.
Error Covariance Adjustment: The process and measurement noise parameters refine the prediction's accuracy by balancing smoothness and responsiveness.
👾 Breakout Bands Calculation:
The breakout bands are derived from the mean absolute error (MAE) of price deviations relative to the filtered trendline:
float upperBand = kalmanPrice + bandMultiplier * mae
float lowerBand = kalmanPrice - bandMultiplier * mae
The multiplier allows traders to adjust the sensitivity of the bands to market volatility.
👾 Slope-Based Trend Detection:
A weighted slope calculation measures the gradient of the filtered price over a configurable window. This slope determines whether the market is trending bullish, bearish, or neutral.
👾 Trailing Stop Mechanism:
The trailing stop employs the Average True Range (ATR) to calculate dynamic stop levels. This ensures positions are protected during volatile moves while minimizing premature exits.
👽 How It Adapts to Price Movements
Dynamic Noise Calibration: By adjusting process and measurement noise inputs, the indicator balances smoothness (to reduce noise) with responsiveness (to adapt to sharp price changes).
Trend Responsiveness: The Kalman Filter ensures that trend changes are quickly identified, while the slope calculation adds confirmation.
Volatility Sensitivity: The MAE-based bands expand and contract in response to changes in market volatility, making them ideal for breakout detection.
👽 How Traders Can Use the Indicator
👾 Breakout Detection:
Bullish Breakouts: When the price moves above the upper band, it signals a potential upward breakout.
Bearish Breakouts: When the price moves below the lower band, it signals a potential downward breakout.
The trailing stop feature offers a dynamic way to lock in profits or minimize losses during trending moves.
👾 Trend Confirmation:
The color-coded Kalman line and slope provide visual cues:
Bullish Trend: Positive slope, green line.
Bearish Trend: Negative slope, red line.
👽 Why It’s Useful for Traders
Dynamic and Adaptive: The indicator adjusts to changing market conditions, ensuring relevance across timeframes and asset classes.
Noise Reduction: The Kalman Filter smooths price data, eliminating false signals caused by short-term noise.
Comprehensive Insights: By combining breakout detection, trend analysis, and risk management, it offers a holistic trading tool.
👽 Indicator Settings
Process Noise (Position & Velocity): Adjusts filter responsiveness to price changes.
Measurement Noise: Defines expected price noise for smoother trend detection.
Slope Window: Configures the lookback for slope calculation.
Lookback Period for MAE: Defines the sensitivity of the bands to volatility.
Band Multiplier: Controls the band width.
ATR Multiplier: Adjusts the sensitivity of the trailing stop.
Line Width: Customizes the appearance of the trailing stop line.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Momentum BandsMomentum Bands indicator-->technical tool that measures the rate of price change and surrounds this momentum with adaptive bands to highlight overbought and oversold zones. Unlike Bollinger Bands, which track price, these bands track momentum itself, offering a unique view of market strength and exhaustion points. At its core, it features a blue momentum line that calculates the rate of change over a set period, an upper red band marking dynamic resistance created by adding standard deviations to the momentum average, a lower green band marking dynamic support by subtracting standard deviations, and a gray middle line representing the average of momentum as a central anchor. When the momentum line touches or moves beyond the upper red band, it often signals that the market may be overbought and a pullback or reversal could follow; traders might lock in profits or watch for short setups. Conversely, when it drops below the lower green band, it can suggest an oversold market primed for a bounce, prompting traders to look for buying opportunities. If momentum remains between the bands, it typically indicates balanced conditions where waiting for stronger signals at the extremes is wise. The indicator can be used in contrarian strategies—buying near the lower band and selling near the upper—or in trend-following setups by waiting for momentum to return toward the centerline before entering trades. For stronger confirmation, traders often combine it with volume spikes, support and resistance analysis, or other trend tools, and it’s useful to check multiple timeframes to spot consistent patterns. Recommended settings vary: short-term traders might use a 7–10 period momentum with 14-period bands; medium-term traders might keep the default 14-period momentum and 20-period bands; while long-term analysis might use 21-period momentum and 50-period bands. Visually, background colors help spot extremes: red for strong overbought, green for strong oversold, and no color for normal markets, alongside reference lines at 70, 30, and 0 to guide traditional overbought, oversold, and neutral zones. Typical bullish signals include momentum rebounding from the lower band, crossing back above the middle after being oversold, or showing divergence where price makes new lows but momentum doesn’t. Bearish signals might appear when momentum hits the upper band and weakens, drops below the middle after being overbought, or price makes new highs while momentum fails to follow. The indicator tends to work best in mean-reverting or sideways markets rather than strong trends, where overbought and oversold conditions tend to repeat.
HPDR Bands IndicatorThe HPDR Bands indicator is a customizable tool designed to help traders visualize dynamic price action zones. By combining historical price ranges with adaptive bands, this script provides clear insights into potential support, resistance, and midline levels. The indicator is well-suited for all trading styles, including trend-following and range-bound strategies.
Features:
Dynamic Price Bands: Calculates price zones based on historical highs and lows, blending long-term and short-term price data for responsive adaptation to current market conditions.
Probability Enhancements: Includes a probability plot derived from the relative position of the closing price within the range, adjusted for volatility to highlight potential price movement scenarios.
Fibonacci-Like Levels: Highlights key levels (100%, 95%, 88%, 78%, 61%, 50%, and 38%) for intuitive visualization of price zones, aiding in identifying high-probability trading opportunities.
Midline Visualization: Displays a midline that serves as a reference for price mean reversion or breakout analysis.
How to Use:
Trending Markets: Use the adaptive upper and lower bands to gauge potential breakout or retracement zones.
Range-Bound Markets: Identify support and resistance levels within the defined price range.
Volatility Analysis: Observe the probability plot and its sensitivity to volatility for informed decision-making.
Important Notes:
This script is not intended as investment advice. It is a tool to assist with market analysis and should be used alongside proper risk management and other trading tools.
The script is provided as-is and without warranty. Users are encouraged to backtest and validate its suitability for their specific trading needs.
Happy Trading!
If you find this script helpful, consider sharing your feedback or suggestions for improvement. Collaboration strengthens the TradingView community, and your input is always appreciated!
Multi-Period % Change Bands (Extreme Dots)Multiple Period Percentage Change Extreme Dots
This indicator visualizes percentage changes across three different timeframes (8, 13, and 21 days), highlighting extreme movements that break out of a user-defined band. It's designed to identify which timeframe is showing the most significant percentage change when prices make notable moves.
Features:
- Tracks percentage changes for 8-day, 13-day, and 21-day periods
- Customizable upper and lower bands to define significant moves
- Shows dots only for the most extreme moves (highest above band or lowest below band)
- Color-coded for easy identification:
- Blue: 8-day changes
- Green: 13-day changes
- Red: 21-day changes
- Includes current values display for all timeframes
Usage Tips:
- Shorter timeframes (8-day) are more sensitive to price changes and should use narrower bands (e.g., ±3%)
- Medium timeframes (13-day) work well with moderate bands (e.g., ±5%)
- Longer timeframes (21-day) can use wider bands (e.g., ±8%)
- Dots appear only when a timeframe shows the most extreme move above/below bands
- Use the gray zone between bands to identify normal price action ranges
The indicator helps identify which lookback period is showing the strongest momentum in either direction, while filtering out normal market noise within the bands.
Note: This is particularly useful for:
- Identifying trend strength across different timeframes
- Spotting which duration is showing the most extreme moves
- Filtering out minor fluctuations through the band system
- Comparing relative strength of moves across different periods
Concretum BandsDefinition
The Concretum Bands indicator recreates the Upper and Lower Bound of the Noise Area described in the paper "Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)" published by Concretum founder Zarattini, along with Barbon and Aziz, in May 2024.
Below we provide all the information required to understand how the indicator is calculated, the rationale behind it and how people can use it.
Idea Behind
The indicator aims to outline an intraday price region where the stock is expected to move without indicating any demand/supply imbalance. When the price crosses the boundaries of the Noise Area, it suggests a significant imbalance that may trigger an intraday trend.
How the Indicator is Calculated
The bands at time HH:MM are computed by taking the open price of day t and then adding/subtracting the average absolute move over the last n days from market open to minute HH:MM . The bands are also adjusted to account for overnight gaps. A volatility multiplier can be used to increase/decrease the width of the bands, similar to other well-known technical bands. The bands described in the paper were computed using a lookback period (length) of 14 days and a Volatility Multiplier of 1. Users can easily adjust these settings.
How to use the indicator
A trader may use this indicator to identify intraday moves that exceed the average move over the most recent period. A break outside the bands could be used as a signal of significant demand/supply imbalance.
Intraday Volatility Bands [Honestcowboy]The Intraday Volatility Bands aims to provide a better alternative to ATR in the calculation of targets or reversal points.
How are they different from ATR based bands?
While ATR and other measures of volatility base their calculations on the previous bars on the chart (for example bars 1954 to 1968). The volatility used in these bands measure expected volatility during that time of the day.
Why would you take this approach?
Markets behave different during certain times of the day, also called sessions.
Here are a couple examples.
Asian Session (generally low volatility)
London Session (bigger volatility starts)
New York Session (overlap of New York with London creates huge volatility)
Generally when using bands or channel type indicators intraday they do not account for the upcoming sessions. On London open price will quickly spike through a bollinger band and it will take some time for the bands to adjust to new volatility.
This script will show expected volatility targets at the start of each new bar and will not adjust during the bar. It already knows what price is expected to do at this time of day.
Script also plots arrows when price breaches either the top or bottom of the bands. You can also set alerts for when this occurs. These are non repainting as the script knows the level at start of the bar and does not change.
🔷 CALCULATION
Think of this script like an ATR but instead it uses past days data instead of previous bars data. Charts below should visualise this more clearly:
The scripts measure of volatility is based on a simple high-low.
The script also counts the number of bars that exist in a day on your current timeframe chart. After knowing that number it creates the matrix used in it's calculations and data storage.
See how it works perfectly on a lower timeframe chart below:
Getting this right was the hardest part, check the coding if you are interested in this type of stuff. I commented every step in the coding process.
🔷 SETTINGS
Every setting of the script has a tooltip but I provided a breakdown here:
Some more examples of different charts:
Rolling Volatility BandsMake sure to view it from the 1D candlestick chart.
The Rolling Volatility Bands indicator provides a statistically-driven approach to visualizing expected daily price movements using true volatility calculations employed by professional options traders. Unlike traditional Bollinger Bands which use price standard deviation around a moving average, this indicator calculates actual daily volatility from log returns over customizable rolling periods (20-day and 60-day), then annualizes the volatility using the standard √252 formula before projecting forward-looking probability bands. The 1 Standard Deviation bands represent a ~68% probability zone where price is expected to trade the following day, while the 2 Standard Deviation bands capture ~95% of expected movements. This methodology mirrors how major exchanges calculate expected moves for earnings and FOMC events, making it invaluable for options strategies like iron condors during low-volatility periods (narrow bands) or directional plays when volatility expands. The indicator works on any timeframe while always utilizing daily candle data via security() calls, ensuring consistent volatility calculations regardless of your chart resolution, and includes real-time annualized volatility percentages plus daily expected range statistics for comprehensive market analysis.
Super-Elliptic BandsThe core of the "Super-Elliptic Bands" indicator lies in its use of a super-ellipse mathematical model to create dynamic price bands around a central Simple Moving Average (SMA). Here's a concise breakdown of its essential components:
Central Moving Average (MA):
A Simple Moving Average (ta.sma(close, maLen)) serves as the baseline, anchoring the bands to the average price over a user-defined period (default: 50 bars).
Super-Ellipse Formula:
The bands are generated using the super-ellipse equation: |y/b| = (1 - |x/a|^p)^(1/p), where:
x is a normalized bar index based on a user-defined cycle period (periodBase, default: 64), scaled to range from -1 to +1.
a = 1 (fixed semi-major axis).
b is the volatility-based semi-minor axis, calculated as volRaw * mult, where volRaw comes from ta.stdev, ta.atr, or ta.tr (user-selectable).
p (shapeP, default: 2.0) controls the band shape:
p = 2: Elliptical bands.
p < 2: Pointier, diamond-like shapes.
p > 2: Flatter, rectangular-like shapes.
This formula creates bands that dynamically adjust their width and shape based on price volatility and a cyclical component.
enjoy....
ka66: Bar Range BandsThis tool takes a bar's range, and reflects it above the high and below the low of that bar, drawing upper and lower bands around the bar. Repeated for each bar. There's an option to then multiply that range by some multiple. Use a value greater than 1 to get wider bands, and less than one to get narrower bands.
This tool stems out of my frustration from the use of dynamic bands (like Keltner Channels, or Bollinger Bands), in particular for estimating take profit points.
Dynamic bands work great for entries and stop loss, but their dynamism is less useful for a future event like taking profit, in my experience. We can use a smaller multiple, but then we can often lose out on a bigger chunk of gains unnecessarily.
The inspiration for this came from a friend explaining an ICT/SMC concept around estimating the magnitude of a trend, by calculating the Asian Session Range, and reflecting it above or below on to the New York and London sessions. He described this as standard deviation of the Asian Range, where the range can thus be multiplied by some multiple for a wider or narrower deviation.
This, in turn, also reminded me of the Measured Move concept in Technical Analysis. We then consider that the market is fractal in nature, and this is why patterns persist in most timeframes. Traders exist across the spectrum of timeframes. Thus, a single bar on a timeframe, is made up of multiple bars on a lower timeframe . In other words, when we reflect a bar's range above or below itself, in the event that in a lower timeframe, that bar fit a pattern whose take profit target could be estimated via a Measured Move , then the band's value becomes a more valid estimate of a take profit point .
Yet another way to think about it, by way of the fractal nature above, is that it is essentially a simplified dynamic support and resistance mechanism , even simpler than say the various Pivot calculations (e.g. Classical, Camarilla, etc.).
This tool in general, can also be used by those who manually backtest setups (and certainly can be used in an automated setting too!). It is a research tool in that regard, applicable to various setups.
One of the pitfalls of manual backtesting is that it requires more discipline to really determine an exit point, because it's easy to say "oh, I'll know more or less where to exit when I go live, I just want to see that the entry tends to work". From experience, this is a bad idea, because our mind subconsciously knows that we haven't got a trained reflex on where to exit. The setup may be decent, but without an exit point, we will never have truly embraced and internalised trading it. Again, I speak from experience!
Thus, to use this to research take profit/exit points:
Have a setup in mind, with all the entry rules.
Plot your setup's indicators, mark your signals.
Use this indicator to get an idea of where to exit after taking an entry based on your signal.
Credits:
@ICT_ID for providing the idea of using ranges to estimate how far a trend move might go, in particular he used the Asian Range projected on to the London and New York market sessions.
All the technicians who came up with the idea of the Measured Move.
Vollinger BandsI'm happy to present to you... VOLLINGER BANDS. Loosely based on bollinger bands, this indicator uses the new Up/Down Volume indicator from tradingview, which I have add moving averages, and a width calculation between them to determine squeeze. Essentially I have created a volume squeeze bollinger band derivative, hence the term "Vollinger Band".
The bands are NOT a deviation of any middle line or moving average, but rather their own moving averages of the volume delta, respectively.
Blue background = Volume Squeeze (vollinger bands width is less than the squeeze strength line), meaning consolidation, and a big move may happen soon.
Top line = A moving average of the Up Volume delta
Bottom line = A moving average of the Down Volume delta
Vol MA = the moving average length of both the top/bottom line
> If you zoom in, you can see a white line, which is the squeeze represented as a single line, calculated using bollinger bands width. The squeeze strength is a moving average of the squeeze line, which then determines if the width is below that moving average, then the squeeze will occur (white line below purple)
The bands are colored based on the sum of the Up/Down volume over the specified number of bars (preset at 5). If the volume is more buying than selling over that amount of bars, then the line is colored green, and vice versa.
[blackcat] L1 Vitali Apirine Exponential Deviation BandsLevel 1
Background
Vitali Apirine’s articles in the July issues on 2019,“Exponential Deviation Bands”
Function
In “Exponential Deviation Bands” in this issue, author Vitali Apirine introduces a price band indicator based on exponential deviation rather than the more traditional standard deviation, such as is used in the well-known Bollinger Bands. As compared to standard deviation bands, the author’s exponential deviation bands apply more weight to recent data and generate fewer breakouts. Apirine describes using the bands as a tool to assist in identifying trends.
Remarks
Feedbacks are appreciated.
BBSS - Bollinger Bands Scalping SignalsModified Bollinger Bands Indicator
Added:
- color change divergence (green) and narrowing (red) of the upper and lower bands
- color change of the moving average - upward trend (green) and downward trend (red)
- the appearance of a potential signal for long and short positions when the candle closes behind the upper or lower bands.
How to use the indicator:
Long conditions:
- the price breaks through the upper band
- Bollinger bands are expanding and should be green
- the mid-line is green
- the trigger candle should be green
Short conditions:
- the price breaks through the lower band
- Bollinger bands are expanding and should be red
- the mid-line is red
- the trigger candle should be red
MTF VWAP & StDev BandsMulti Timeframe Volume Weighted Average Price with Standard Deviation Bands
I used the script "Koalafied VWAP D/W/M/Q/Y" by Koalafied_3 and made some changes, such as adding more standard deviation bands.
The script can display the daily, weekly, monthly, quarterly and yearly VWAP.
Standard deviation bands values can be changed (default values are 0.618, 1, 1.618, 2, 2.618, 3).
Also the previous standard deviation bands can be displayed.
