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)
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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
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.
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.
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.
Bollinger Bands (SMA) with Trend Filtered Buy/SellOverview
This indicator is a trend-following Bollinger Bands tool based on SMA, enhanced with a 200 SMA filter to display BUY/SELL signals only in the direction of the prevailing trend.
Instead of showing every possible reversal, it focuses on high-probability entries aligned with the trend.
Key Features
Feature Description
Bollinger Bands (SMA) Plots upper, lower, and middle bands using Simple Moving Average (SMA) and standard deviation.
200 SMA Trend Filter Determines the overall market trend (bullish or bearish).
BUY/SELL Signals Generates signals when price reacts from Bollinger Bands.
Trend Filtering Only BUY signals above the 200 SMA, only SELL signals below the 200 SMA.
Alert Function TradingView alerts can be triggered when a signal occurs.
Toggle ON/OFF Option to enable or disable signal display.
Signal Logic
BUY Signal
Price is above the 200 SMA (uptrend)
Previous candle closed below the lower Bollinger Band
Current candle closes back inside the band → Confirmed rebound → BUY signal
SELL Signal
Price is below the 200 SMA (downtrend)
Previous candle closed above the upper Bollinger Band
Current candle closes back inside the band → Confirmed pullback → SELL signal
How to Use
Trend-Following Entries:
Enter trades only in the trend direction, improving accuracy and reducing countertrend trades.
Filter Out False Signals:
The 200 SMA filter removes noise from opposite-trend signals.
Alerts:
Receive notifications when a valid BUY/SELL setup appears without watching the chart constantly.
This indicator is ideal for traders who want to focus on high-probability trend-following setups, especially in markets like Forex or Gold, where strong one-way moves often occur.
このインジケーターは、SMAベースのボリンジャーバンドにトレンドフィルター(200SMA)を追加し、トレンドフォロー型のBUY/SELLシグナルを表示するツールです。
短期の逆張りではなく、大きなトレンド方向に沿ったシグナルだけを出すように設計されています。
主な機能
機能 説明
ボリンジャーバンド (SMA) 期間を指定した単純移動平均(SMA)を基準に、標準偏差で上下のバンドを表示
200SMA(トレンド判定) 現在の相場が上昇トレンドか下降トレンドかを判断
BUY/SELLシグナル ボリンジャーバンドの反発を検出してシグナル表示
トレンドフィルター 200SMAより上ならBUYのみ、200SMAより下ならSELLのみ表示
アラート機能 BUY/SELLシグナル発生時にTradingViewのアラートで通知可能
ON/OFF切替 BUY/SELLシグナルの表示はスイッチでON/OFF可能
シグナルロジック
BUYシグナル
200SMAより上にいる
前の足で価格がボリンジャーバンド下限を下抜け
現在の足でバンド内に戻る → 反発確認 → BUYシグナル表示
SELLシグナル
200SMAより下にいる
前の足で価格がボリンジャーバンド上限を上抜け
現在の足でバンド内に戻る → 反落確認 → SELLシグナル表示
トレードでの使い方
トレンドフォロー型エントリー
→ 200SMAを基準に、相場の方向に沿ったエントリーだけを狙う
逆張りのフィルタリング
→ トレンドに逆らう無駄なシグナルを表示しない
アラート通知
→ チャートを見ていなくても、シグナル発生時に通知可能
このインジケーターは「トレンドフォローの精度を高めたいトレーダー」向けです。
特にゴールドやFXで、一方向の強いトレンドが出やすい相場で有効です。
VWAP with Prev. Session BandsVWAP with Prev. Session Bands is an advanced indicator based on TradingView’s original VWAP. It adds configurable standard deviation or percentage-based bands, both for the current and previous session. You can anchor the VWAP to various timeframes or events (like Sessions, Weeks, Months, Earnings, etc.) and selectively show up to three bands.
The unique feature of this script is the ability to display the VWAP and bands from the previous session, helping traders visualize mean reversion levels or historical volatility ranges.
Built on top of the official TradingView VWAP implementation, this version provides enhanced flexibility and visual clarity for intraday and swing traders alike.
Ethereum Logarithmic Regression Bands (Fine-Tuned)This indicator, "Ethereum Logarithmic Regression Bands (Fine-Tuned)," is my attempt to create a tool for estimating long-term trends in Ethereum (ETH/USD) price action using logarithmic regression bands. Please note that I am not an expert in financial modeling or coding—I developed this as a personal project to serve as a rough estimation rather than a precise or professional trading tool. The data was fitted to non-bubble periods of Ethereum's history to provide a general trendline, but it’s far from perfect.
I’m sharing this because I couldn’t find a similar indicator available, and I thought it might be useful for others who are also exploring ETH’s long-term behavior. The bands start from Ethereum’s launch price and are adjustable via input parameters, but they are based on my best effort to align with historical data. With some decent coding experience, I’m sure someone could refine this further—perhaps by optimizing the coefficients or incorporating more advanced fitting techniques. Feel free to tweak the code, suggest improvements, or use it as a starting point for your own projects!
How to Use:
** THIS CHART IS SPECIFICALLY CODED FOR ETH/USD (KRAKEN) ON THE WEEKLY TIMEFRAME IN LOG VIEW**
The main band (blue) represents the logarithmic regression line.
The upper (red) and lower (green) bands provide a range around the main trend, adjustable with multipliers.
Adjust the "Launch Price," "Base Coefficient," "Growth Coefficient," and other inputs to experiment with different fits.
Disclaimer:
This is not financial advice. Use at your own risk, and always conduct your own research before making trading decisions.
Bollinger Bands + RSI StrategyThe Bollinger Bands + RSI strategy combines volatility and momentum indicators to spot trading opportunities in intraday settings. Here’s a concise summary:
Components:
Bollinger Bands: Measures market volatility. The lower band signals potential buying opportunities when the price is considered oversold.
Relative Strength Index (RSI): Evaluates momentum to identify overbought or oversold conditions. An RSI below 30 indicates oversold, suggesting a buy, and above 70 indicates overbought, suggesting a sell.
Strategy Execution:
Buy Signal : Triggered when the price falls below the lower Bollinger Band while the RSI is also below 30.
Sell Signal : Activated when the price exceeds the upper Bollinger Band with an RSI above 70.
Exit Strategy : Exiting a buy position is considered when the RSI crosses back above 50, capturing potential rebounds.
Advantages:
Combines price levels with momentum for more reliable signals.
Clearly defined entry and exit points help minimize emotional trading.
Considerations:
Can produce false signals in very volatile or strongly trending markets.
Best used in markets without a strong prevailing trend.
This strategy aids traders in making decisions based on technical indicators, enhancing their ability to profit from short-term price movements.
Bollinger Bands Weighted Alert System (BBWAS)The idea of this indicator is very similar to my previous published script called BBAS (Bollinger Bands Alert System).
Just with little additions. In this case, we're using a Weighted Moving Average (ta.wma) instead of Simple Moving Average to calculate the basis line.
A breakout in trading refers to a situation where the price of a security or asset moves beyond a defined level of support or resistance, which is typically indicated by technical analysis tools like Bollinger Bands. Bollinger Bands consist of three lines: the upper band, the lower band, and the middle band (or basis). The upper and lower bands are set at a specified number of standard deviations away from the middle band, and they help to define the range within which the price of an asset is expected to fluctuate.
When the price of the asset moves beyond the upper or lower band, it is said to have "broken out" of the range. If the price closes below the lower band, it is considered a bearish breakout, and if it closes above the upper band, it is considered a bullish breakout.
Once a breakout occurs, traders may look for a confirmation signal before entering a trade. In this case, crossing the middle line (or basis) after a breakout may signal a potential trend reversal and a good opportunity to enter a long or short trade, depending on the direction of the breakout.
Dear traders, while we strive to provide you with the best trading tools and resources, we want to remind you to exercise caution and diligence in your investing decisions.
It is important to always do your own research and analysis before making any trades. Remember, the responsibility for your investments ultimately lies with you.
Happy trading!
DEMA Supertrend Bands [Misu]█ Indicator based on DEMA (Double Exponential Moving Average) & Supertrend to show Bands .
DEMA attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
Supertrend aims to detect price trends, it's also used to set protective stops.
█ Usages:
Combining Dema to calculate Supertrend results in nice lower and upper bands.
This can be used to identify potential supports and resistances and set protective stops.
█ Parameters:
Length DEMA: Double Ema lenght used to calculate DEMA. Dema is used by Supertrend indicator.
Length Atr: Atr lenght used to calculate Atr. Atr is used by Supertrend indicator.
Band Mult: Used to calculate Supertrend Bands width.
█ Other Applications:
The mid band can be used to filter bad signals in the manner of a more classical Moving Average.