Dynamic Ray BandsAbout Dynamic Ray Bands
Dynamic Ray Bands is a volatility-adaptive envelope indicator that adjusts in real time to evolving market conditions. It uses a Double Exponential Moving Average (DEMA) as its central trend reference, with upper and lower bands scaled according to current volatility measured by the Average True Range (ATR).
This creates a dynamic structure that visually frames price action, helping traders identify areas of potential trend continuation, overextension, or mean reversion.
How It Works
🟡 Centerline (DEMA)
The central yellow line is a Double Exponential Moving Average, which offers a smoother, less laggy trend signal than traditional moving averages. It represents the market’s short- to medium-term “equilibrium.”
🔵 Outer Bands
Plotted at:
Upper Band = DEMA + (ATR × outerMultiplier)
Lower Band = DEMA - (ATR × outerMultiplier)
These bands define the extreme bounds of current volatility. When price breaks above or below them, it can signal strong directional momentum or overbought/oversold conditions, depending on context. They're often used as trend breakout zones or to time exits after extended runs.
🟣 Inner Bands
Plotted closer to the DEMA:
Inner Upper = DEMA + (ATR × innerMultiplier)
Inner Lower = DEMA - (ATR × innerMultiplier)
These are preliminary volatility thresholds, offering early cues for potential expansion or reversal. They may be used for scalping, tight stop zones, or pre-breakout positioning.
🔁 Dynamic Width (Bands are Dynamically Adjusted Per Tick)
The width of both inner and outer bands is based on ATR (Average True Range), which is recalculated in real time. This means:
During high volatility, the bands expand, allowing for wider price fluctuations.
During low volatility, the bands contract, tightening range expectations.
Unlike fixed-width channels or standard Bollinger Bands (which use standard deviation), this per-tick adjustment via ATR enables Dynamic Ray Bands to reduce false signals in choppy markets and remain more reactive during trending conditions.
⚙️ Inputs
DMA Length — Period for the central DEMA.
ATR Length — Lookback used for ATR volatility calculations.
Outer Band Multiplier — Controls sensitivity of extreme bands.
Inner Band Multiplier — Controls proximity of inner bands.
Show Inner Bands — Toggle for plotting the inner zone.
🔔 Alerts
Alert conditions are included for:
Price closing above/below the outer bands (trend momentum or overextension)
Price closing above/below the inner bands (early signs of strength/weakness)
🧭 Use Cases
Breakout detection — Catch price continuation beyond the outer bands.
Volatility filtering — Adjust trade logic based on band width.
Mean reversion — Monitor for snapbacks toward the DEMA after price stretches too far.
Trend guidance — Use band slope and price position to confirm direction.
⚠️ Disclaimer
This script is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to trade any specific market or security. Always test indicators thoroughly before using them in live trading.
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Bollinger Bands Forecast with Signals (Zeiierman)█ Overview
Bollinger Bands Forecast with Signals (Zeiierman) extends classic Bollinger Bands into a forward-looking framework. Instead of only showing where volatility has been, it projects where the basis (midline) and band width are likely to drift next, based on recent trend and volatility behavior.
The projection is built from the measured slopes of the Bollinger basis, the standard deviation (or ATR, depending on the mode), and a volatility “breathing” component. On top of that, the script includes an optional projected price path that can be blended with a deterministic random walk, plus rejection signals to highlight failed band breaks.
█ How It Works
⚪ Bollinger Core
The script first computes standard Bollinger Bands using the selected Source, Length, and Multiplier:
Basis = SMA(Source, Length)
Band width = Multiplier × StDev(Source, Length)
Upper/Lower = Basis ± Width
This remains the “live” (non-forecast) structure on the chart.
⚪ Trend & Volatility Slope Estimation
To project forward, the indicator measures directional drift and volatility drift using linear regression differences:
Basis slope from the Bollinger basis
StDev slope from the Bollinger deviation
ATR slope for ATR-based projection mode
These slopes drive the forecast bands forward, reflecting the market’s recent directional and volatility regime.
⚪ Projection Engine (Forecast Bands)
At the last bar, the indicator draws projected basis, upper, and lower lines out to Forecast Bars. The projected basis can be:
Trend (straight linear projection)
Curved (ease-in/out transition toward projected endpoints)
Smoothed (extra smoothing on projected basis/width)
⚪ Price Path Projection + Optional Random Walk
In addition to projecting the bands, the script can draw a price forecast path made of a small number of zigzag swings.
Each swing targets a point offset from the projected basis by a multiple of the projected half-width (“width units”).
Decay gradually reduces swing size as the forecast deepens.
The Optional Random Walk Blend adds a deterministic drift component to the zigzag path. It’s not true randomness; it’s a stable pseudo-random sequence, so the drawing doesn’t jump around on refresh, while still adding “natural” variation.
⚪ Rejection Signals
Signals are based on failed attempts to break a band:
Bear Signal (Down): price tries to push above the upper band, then falls back inside, while still closing above the basis.
Bull Signal (Up): price tries to push below the lower band, then returns back inside, while still closing below the basis.
█ How to Use
⚪ Forward Support/Resistance Corridors
Treat the projected upper/lower bands as a future volatility envelope, not a guarantee:
The upper projection ≈ is likely a resistance level if the regime persists
The lower projection ≈ is likely a support level if the regime persists
Best used for trade planning, targets, and “where price could travel” under similar conditions.
⚪ Regime Read: Trend + Volatility
The projection shape is informative:
Rising basis + expanding width → trend with increasing volatility (needs wider stops / more caution)
Flat basis + compressing width → contraction regime (often precedes expansion)
⚪ Signals for Mean-Reversion / Failed Breakouts
The rejection markers are useful for fade-style setups:
A Down signal near/after upper-band failure can imply rotation back toward the basis.
An Up signal near/after lower-band failure can imply snap-back toward the basis.
With MA filtering enabled, signals are constrained to align with the broader bias, helping reduce chop-driven noise.
█ Related Publications
Donchian Predictive Channel (Zeiierman)
█ Settings
⚪ Bollinger Band
Controls the live Bollinger Bands on the chart.
Source – Price used for calculations.
Length – Lookback period; higher = smoother, lower = more reactive.
Multiplier – Bandwidth; higher = wider bands, lower = tighter bands.
⚪ Forecast
Controls the forward projection of the Bollinger Bands.
Forecast Bars – How far into the future the bands are projected.
Trend Length – Lookback used to estimate trend and volatility slopes.
Forecast Band Mode – Defines projection behavior (linear, curved, breathing, ATR-based, or smoothed).
⚪ Price Forecast
Controls the projected price path inside the bands.
ZigZag Swings – Number of projected oscillations.
Amplitude – Distance from basis, measured in bandwidth units.
Decay – Shrinks swings further into the forecast.
⚪ Random-Walk
Adds controlled randomness to the price path.
Enable – Toggle random-walk influence.
Blend – Strength of randomness vs. zigzag.
Step Size – Size of random steps (band-width units).
Decay – Reduces randomness as the forecast deepens.
Seed – Changes the (stable) random sequence.
⚪ Signals
Controls rejection/mean-reversion signals.
Show Signals – Enable/disable signal markers.
MA Filter (Type/Length) – Filters signals by trend direction.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
RSI Dynamic Bands█ OVERVIEW
The "RSI Dynamic Bands" indicator is a variant of the Relative Strength Index (RSI) oscillator that brings its signals directly onto the price chart. It displays dynamic bands around the price, adjusted based on RSI levels, enabling easy identification of potential overbought or oversold conditions. The indicator also integrates a multi-timeframe RSI table, facilitating the analysis of trend strength across different timeframes.
█ CONCEPTS
The "RSI Dynamic Bands" indicator is designed to simplify the interpretation of price levels in the context of support and resistance zones, which can be correlated with other technical indicators and RSI values. Since the price itself does not display RSI values, a table showing RSI for four selected timeframes has been added, allowing traders to quickly assess trend strength across different time intervals. The most effective approach is to combine the indicator with other technical analysis tools, such as Fibonacci levels or pivot points, to confirm signals when the price approaches the bands and RSI values indicate a potential reversal.
Band Calculation
The bands are calculated based on the current closing price and RSI values, incorporating dynamic scaling to better adapt to market conditions. The formulas for the bands are as follows:
• Upper Band: close + (rsiUpper - rsi) * scaleFactor, where rsiUpper is the upper RSI level (default: 70), and scaleFactor accounts for market volatility.
• Lower Band: close + (rsiLower - rsi) * scaleFactor, where rsiLower is the lower RSI level (default: 30).
• Midline: The arithmetic average of the upper and lower bands: (upperBand + lowerBand) / 2.
Why Scaling? Without scaling, the bands would be chaotic and jagged, making them difficult to interpret. Scaling smooths the bands, making them wider during periods of high volatility and narrower during consolidation, better reflecting potential support and resistance levels.
Indicator Features
• Dynamic Price Bands: The bands adapt to market conditions, facilitating the identification of key price levels.
• Multi-Timeframe RSI Table: Displays RSI values for four selected timeframes (default: 15m, 1h, 4h, Daily), enabling comparison of trend strength across different perspectives.
• Style Customization: Users can adjust band colors, line thickness, and toggle the visibility of bands, fills, and the table.
How to Set Up the Indicator
1 — Add the "RSI Dynamic Bands" indicator to your TradingView chart.
2 — Configure parameters in the settings, such as RSI length, upper/lower levels, and scaling multiplier, to match your trading style.
3 — Enable or disable the display of bands, fills, or the RSI table based on your needs.
4 — Adjust band and table colors in the input section and line thickness in the "Style" section to better align the indicator with your chart.
█ OTHER SECTIONS
FEATURES
• RSI Length: The period for calculating RSI (default: 14).
• RSI Levels: Thresholds for overbought (default: 70) and oversold (default: 30).
• Scaling Multiplier: Adjusts bands based on market volatility (default: 0.15).
• Table Timeframes: Select four timeframes for the RSI table (default: 15m, 1h, 4h, Daily).
• Style Options: Customize band colors, fills, table, and line thickness.
HOW TO USE
Add the indicator to your chart, configure the parameters, and observe price interactions with the bands to identify potential entry and exit points. The RSI table allows you to compare RSI values across different timeframes, aiding in trading decisions. The most effective approach is to combine the indicator with other technical analysis tools, such as Fibonacci levels or pivot points, to confirm signals when the price approaches the bands and RSI values indicate a potential reversal.
Trading Strategies:
• Scalping: Use lower timeframes (e.g., 5m, 15m) in the RSI table to quickly identify short-term lows and highs. Wait for the price to approach the lower band in the RSI oversold zone, with RSI on lower timeframes starting to rise, and other tools, such as Fibonacci levels (e.g., 38.2%) or pivot points, confirming support.
• Medium-Term Trading: Focus on 1h and 4h timeframes. Look for confirmation of a low on a lower timeframe (e.g., 1h), where RSI indicates oversold conditions or starts rising, then check if RSI on a higher timeframe (e.g., 4h) confirms the trend. Confirmation from other tools, such as a Fibonacci level (e.g., 50%) or pivot point near the bands, strengthens the signal.
• Long-Term Trading: Use Daily and higher timeframes (e.g., Weekly). Wait for all relevant timeframes to confirm a low (e.g., RSI near oversold and price at the lower band), with lower timeframes (e.g., 4h) showing rising RSI. Other tools, such as Fibonacci levels (e.g., 61.8%) or pivot points near the bands, can further confirm a trend reversal signal.
Fair value bands / quantifytools— Overview
Fair value bands, like other band tools, depict dynamic points in price where price behaviour is normal or abnormal, i.e. trading at/around mean (price at fair value) or deviating from mean (price outside fair value). Unlike constantly readjusting standard deviation based bands, fair value bands are designed to be smooth and constant, based on typical historical deviations. The script calculates pivots that take place above/below fair value basis and forms median deviation bands based on this information. These points are then multiplied up to 3, representing more extreme deviations.
By default, the script uses OHLC4 and SMA 20 as basis for the bands. Users can form their preferred fair value basis using following options:
Price source
- Standard OHLC values
- HL2 (High + low / 2)
- OHLC4 (Open + high + low + close / 4)
- HLC3 (High + low + close / 3)
- HLCC4 (High + low + close + close / 4)
Smoothing
- SMA
- EMA
- HMA
- RMA
- WMA
- VWMA
- Median
Once fair value basis is established, some additional customization options can be employed:
Trend mode
Direction based
Cross based
Trend modes affect fair value basis color that indicates trend direction. Direction based trend considers only the direction of the defined fair value basis, i.e. pointing up is considered an uptrend, vice versa for downtrend. Cross based trends activate when selected source (same options as price source) crosses fair value basis. These sources can be set individually for uptrend/downtrend cross conditions. By default, the script uses cross based trend mode with low and high as sources.
Cross based (downtrend not triggered) vs. direction based (downtrend triggered):
Threshold band
Threshold band is calculated using typical deviations when price is trading at fair value basis. In other words, a little bit of "wiggle room" is added around the mean based on expected deviation. This feature is useful for cross based trends, as it allows filtering insignificant crosses that are more likely just noise. By default, threshold band is calculated based on 1x median deviation from mean. Users can increase/decrease threshold band width via input menu for more/less noise filtering, e.g. 2x threshold band width would require price to cross wiggle room that is 2x wider than typical, 0x erases threshold band altogether.
Deviation bands
Width of deviation bands by default is based on 1x median deviations and can be increased/decreased in a similar manner to threshold bands.
Each combination of customization options produces varying behaviour in the bands. To measure the behaviour and finding fairest representation of fair and unfair value, some data is gathered.
— Fair value metrics
Space between each band is considered a lot, named +3, +2, +1, -1, -2, -3. For each lot, time spent and volume relative to volume moving average (SMA 20) is recorded each time price is trading in a given lot:
Depending on the asset, timeframe and chosen fair value basis, shape of the distributions vary. However, practically always time is distributed in a normal bell curve shape, being highest at lots +1 to -1, gradually decreasing the further price is from the mean. This is hardly surprising, but it allows accurately determining dynamic areas of normal and abnormal price behaviour (i.e. low risk area between +1 and -1, high risk area between +-2 to +-3). Volume on the other hand is typically distributed the other way around, being lowest at lots +1 to -1 and highest at +-2 to +-3. When time and volume are distributed like so, we can conclude that 1) price being outside fair value is a rare event and 2) the more price is outside fair value, the more anomaly behaviour in volume we tend to find.
Viewing metric calculations
Metric calculation highlights can be enabled from the input menu, resulting in a lot based coloring and visibility of each lot counter (time, cumulative relative volume and average relative volume) in data window:
— Alerts
Available alerts are the following:
Individual
- High crossing deviation band (bands +1 to +3 )
- Low crossing deviation band (bands -1 to -3 )
- Low at threshold band in an uptrend
- High at threshold band in a downtrend
- New uptrend
- New downtrend
Grouped
- New uptrend or downtrend
- Deviation band cross (+1 or -1)
- Deviation band cross (+2 or -2)
- Deviation band cross (+3 or -3)
— Practical guide
Example #1 : Risk on/risk off trend following
Ideal trend stays inside fair value and provides sufficient cool offs between the moves. When this is the case, fair value bands can be used for sensible entry/exit levels within the trend.
Example #2 : Mean reversions
When price shows exuberance into an extreme deviation, followed by a stall and signs of exhaustion (wicks), an opportunity for mean reversion emerges. The higher the deviation, the more volatility in the move, the more signalling of exhaustion, the better.
Example #3 : Tweaking bands for desired behaviour
The faster the length of fair value basis, the more momentum price needs to hit extreme deviation levels, as bands too are moving faster alongside price. Decreasing fair value basis length typically leads to more quick and aggressive deviations and less steady trends outside fair value.
Hikaru BandsHikaru Bands is a volatility indicator designed to provide a view of market dynamics. Unlike traditional banding tools like Bollinger Bands, which rely solely on standard deviation, Hikaru Bands incorporate a Composite Volatility Index (UVI). This index is built from a customizable blend of up to ten different technical indicators, including momentum, trend, and risk metrics.
Core Concept & Calculation
The script first calculates the values of up to ten different technical indicators, which you can enable or disable individually. These include RSI, CCI, Sharpe Ratio, Omega Ratio, Z-Score, Rate of Change (ROC), and more. Each selected indicator's output is then normalized into a percentile rank (a scale of 0-100) to ensure they can be compared and combined effectively. Finally, the normalized values are weighted and averaged to create a single Universal Volatility Index (UVI). A high UVI suggests strong bullish momentum and volatility/overbought, while a low UVI suggests strong bearish momentum/oversold.
How to Use & Interpretation
Interpreting the bands is intuitive and provides multiple layers of analysis:
Extreme Bands (Outer Bands): When the price touches or exceeds these bands, it suggests a potential exhaustion point or a climax in the current trend. These are often areas to watch for potential reversals or pullbacks.
Warning Bands: These act as an early signal that momentum is becoming stretched. Price action within this zone indicates a strong trend that may be approaching overbought or oversold territory.
Neutral Bands: The area between these bands and the basis line represents typical price action. When the price remains within this zone, it often signals a consolidating or ranging market.
Features & Customization
This script offers extensive customization to tailor the indicator to your specific needs and analysis style:
Modular Component Selection: Individually enable or disable any of the ten underlying indicators to build your own custom UVI. You can also adjust the weight of each component to give more importance to the indicators you trust most.
Detailed Parameter Control: Fine-tune the settings for each individual indicator, such as the period for RSI, the lookback for the Sharpe Ratio, or the fast/slow lengths for the EMA Spread.
Visuals: Comes with eight built-in color schemes (including Classic, Neon, and Ocean) to match your chart's aesthetic.
Band Smoothing: Apply an optional smoothing filter to the bands and the basis line to reduce noise and focus on the underlying trend.
Disclaimer
This tool is designed for technical analysis and should not be used as a standalone signal for trading. The effectiveness of the bands depends on the selected components and market conditions. Always use this indicator in conjunction with other forms of analysis and a robust risk management strategy.
Dynamic Bollinger Bands with Momentum and Volume (DBBMV)Overview
The Dynamic Bollinger Bands with Momentum and Volume (DBBMV) indicator enhances the traditional Bollinger Bands by dynamically adjusting their width and position based on momentum and volume. This provides a more responsive and context-aware indication of price volatility and potential reversals.
Key Features
Momentum Adjusted Bands: Adjusts the bands' width based on the momentum indicator, reflecting the rate of change in price.
Volume Weighted Bands: Further adjusts the bands based on trading volume to reflect market activity and price volatility.
Signal Alerts: Provides buy and sell signals based on price action relative to the dynamic bands, helping traders identify entry and exit points.
Customizable Parameters: Allows users to adjust the lookback period, momentum sensitivity, and volume weighting for personalized analysis.
How It Works
The DBBMV indicator starts with the traditional Bollinger Bands, which are calculated using a moving average and standard deviation of the selected price source. The width of these bands is then adjusted based on the momentum of the price, making them more sensitive to price changes. Further adjustments are made based on trading volume, which ensures that the bands accurately reflect current market conditions. This results in a set of dynamic Bollinger Bands that provide more nuanced insights into price volatility and potential reversals.
Usage Instructions
Identify Volatile Periods: Use the dynamically adjusted bands to identify periods of high and low volatility in the market.
Spot Reversals: Look for buy signals when the price crosses above the lower band and sell signals when the price crosses below the upper band.
Adjust Sensitivity: Customize the lookback period, momentum sensitivity, and volume weighting to fine-tune the indicator to your specific trading strategy and market conditions.
Enhance Analysis: Combine the DBBMV indicator with other technical analysis tools for a more comprehensive market analysis.
Volume Confirmation: Use the volume-weighted adjustments to confirm the strength of price movements and potential breakouts.
The Dynamic Bollinger Bands with Momentum and Volume (DBBMV) indicator provides traders with a powerful tool to understand market dynamics better and make informed trading decisions based on adjusted volatility and market activity.
Alpine Predictive BandsAlpine Predictive Bands - ADX & Trend Projection is an advanced indicator crafted to estimate potential price zones and trend strength by integrating dynamic support/resistance bands, ADX-based confidence scoring, and linear regression-based price projections. Designed for adaptive trend analysis, this tool combines multi-timeframe ADX insights, volume metrics, and trend alignment for improved confidence in trend direction and reliability.
Key Calculations and Components:
Linear Regression for Price Projection:
Purpose: Provides a trend-based projection line to illustrate potential price direction.
Calculation: The Linear Regression Centerline (LRC) is calculated over a user-defined lookbackPeriod. The slope, representing the rate of price movement, is extended forward using predictionLength. This projected path only appears when the confidence score is 70% or higher, revealing a white dotted line to highlight high-confidence trends.
Adaptive Prediction Bands:
Purpose: ATR-based bands offer dynamic support/resistance zones by adjusting to volatility.
Calculation: Bands are calculated using the Average True Range (ATR) over the lookbackPeriod, multiplied by a volatilityMultiplier to adjust the width. These shaded bands expand during higher volatility, guiding traders in identifying flexible support/resistance zones.
Confidence Score (ADX, Volume, and Trend Alignment):
Purpose: Reflects the reliability of trend projections by combining ADX, volume status, and EMA alignment across multiple timeframes.
ADX Component: ADX values from the current timeframe and two higher timeframes assess trend strength on a broader scale. Strong ADX readings across timeframes boost the confidence score.
Volume Component: Volume strength is marked as “High” or “Low” based on a moving average, signaling trend participation.
Trend Alignment: EMA alignment across timeframes indicates “Bullish” or “Bearish” trends, confirming overall trend direction.
Calculation: ADX, volume, and trend alignment integrate to produce a confidence score from 0% to 100%. When the score exceeds 70%, the white projection line is activated, underscoring high-confidence trend continuations.
User Guide
Projection Line: The white dotted line, which appears only when the confidence score is 70% or higher, highlights a high-confidence trend.
Prediction Bands: Adaptive bands provide potential support/resistance zones, expanding with market volatility to help traders visualize price ranges.
Confidence Score: A high score indicates a stronger, more reliable trend and can support trend-following strategies.
Settings
Prediction Length: Determines the forward length of the projection.
Lookback Period: Sets the data range for calculating regression and ATR.
Volatility Multiplier: Adjusts the width of bands to match volatility levels.
Disclaimer: This indicator is for educational purposes and does not guarantee future price outcomes. Additional analysis is recommended, as trading carries inherent risks.
Uptrick: Logarithmic Crypto Bands
Description :
Introduction
The `Uptrick: Logarithmic Crypto Bands` indicator introduces an innovative approach to technical analysis tailored specifically for the cryptocurrency markets. By leveraging logarithmic transformations combined with dynamic exponential bands, this indicator offers a sophisticated method for identifying critical support and resistance levels, assessing market trends, and evaluating volatility. Its unique approach stands out from traditional indicators by addressing the specific challenges of high volatility and erratic price movements inherent in cryptocurrency trading.
Originality and Usefulness
** 1. Unique Logarithmic Transformation: **
- Innovation : Unlike traditional indicators that often use raw price data, the Uptrick: Logarithmic Crypto Bands applies a logarithmic transformation to the closing prices: logPrice = math.log(close). This approach is original because it reduces the impact of extreme price fluctuations, providing a smoother and more stable price series. This transformation addresses a common issue in cryptocurrency markets where large price swings can obscure true market trends.
- Advantage : The logarithmic transformation compresses the price range, which allows traders to better identify long-term trends and reduce the noise caused by outlier price movements. This results in a more reliable basis for analysis and enhances the ability to detect meaningful market patterns.
**2. Dynamic Exponential Bands :**
- Innovation : The indicator employs exponential calculations to derive dynamic support and resistance levels based on a central base line : baseLine * math.pow(multiplier, n). Unlike static bands that remain fixed regardless of market conditions, these bands adjust dynamically according to market volatility.
- Advantage : The dynamic nature of the bands provides a more responsive and adaptive tool for traders. As market volatility changes, the bands widen or narrow accordingly, offering a more accurate reflection of potential support and resistance levels. This adaptability improves the tool's effectiveness in varying market conditions compared to static or traditional bands.
Detailed Description and Substantiation
**1. Logarithmic Price Calculation :**
- Code : ` logPrice = math.log(close)
- Description : This calculation converts the closing price into its logarithmic value. By compressing the price range, it minimizes the distortion caused by extreme price movements, which can be particularly pronounced in the volatile cryptocurrency markets.
- Purpose : To provide a stabilized price series that facilitates more accurate trend analysis and reduces the influence of erratic price fluctuations.
**2. Moving Averages of Logarithmic Prices :**
- ** Long-Term Moving Average :**
- Code : maLongLogPrice = ta.sma(logPrice, longLength)
longLength = 2000
- ** Description : A simple moving average of the logarithmic price over a long period. This average helps filter out short-term noise and provides insight into the long-term market trend.
- Purpose : To offer a perspective on the overall market direction, making it easier to identify enduring trends and distinguish them from short-term price movements.
- Short-Term Moving Average :
- Code : maShortLogPrice = ta.sma(logPrice, shortLength) shortLength = 900
- Description : A simple moving average of the logarithmic price over a shorter period. This component captures more immediate price trends and potential reversal points.
- Purpose : To detect short-term trends and changes in market direction, allowing traders to make timely trading decisions based on recent price action.
**3. Base Line Calculation :**
- Code : baseLine = math.exp(maShortLogPrice)
- Description : Converts the short-term moving average of the logarithmic price back to the original price scale. This base line serves as the central reference point for calculating the surrounding bands.
- Purpose : To establish a benchmark level from which the exponential bands are calculated, providing a central reference for assessing potential support and resistance levels.
**4. Band Calculation and Plotting :**
- ** Code :**
- Band 1: plot(baseLine * math.pow(multiplier, 1), color=color.new(color.yellow, 20), linewidth=1, title="Band 1")
- Band 2: plot(baseLine * math.pow(multiplier, 2), color=color.new(color.yellow, 20), linewidth=1, title="Band 2")
- Band 3: plot(baseLine * math.pow(multiplier, 3), color=color.new(color.yellow, 20), linewidth=1, title="Band 3")
- Band 4: plot(baseLine * math.pow(multiplier, 4), color=color.new(color.yellow, 20), linewidth=1, title="Band 4")
- Band 5: plot(baseLine * math.pow(multiplier, 5), color=color.new(color.yellow, 10), linewidth=1, title="Band 5")
- Band 6: plot(baseLine * math.pow(multiplier, 6), color=color.new(color.yellow, 0), linewidth=1, title="Band 6")
- * Multiplier : Set at 1.3, adjusts the spacing between bands to accommodate varying levels of market volatility.
- Description : Bands are plotted at exponential intervals from the base line. Each band represents a potential support or resistance level, with the spacing between them increasing exponentially. The color opacity of each band indicates its level of significance, with closer bands being more relevant for immediate trading decisions.
** How to Use the Indicator :**
**1. Identifying Support and Resistance Levels :**
- Support Levels : The lower bands, closer to the base line, can act as potential support levels. When the price approaches these bands from above, they may indicate areas where the price could stabilize or reverse direction.
- Resistance Levels : The upper bands, further from the base line, serve as resistance levels. When the price nears these bands from below, they can act as barriers to price movement, potentially leading to reversals or stalls.
**2. Confirming Trends :**
- Uptrend Confirmation : When the price consistently remains above the base line and moves towards higher bands, it signals a strong bullish trend. This confirmation helps traders capitalize on upward price movements.
- Downtrend Confirmation : When the price stays below the base line and approaches lower bands, it indicates a bearish trend. This confirmation assists traders in acting on downward price movements.
3. Analyzing Volatility :
- Wide Bands : Wider spacing between bands reflects higher market volatility. This indicates a more turbulent trading environment, where price movements are less predictable. Traders may need to adjust their strategies to handle increased volatility.
- Narrow Bands : Narrower bands suggest lower volatility and a more stable market environment. This can result in more predictable price movements and clearer trading signals.
**4. Entry and Exit Points :**
- Entry Points : Consider buying when the price bounces off the base line or a band, which could signal support in an uptrend.
- Exit Points : Evaluate selling or taking profits when the price nears upper bands or shows signs of reversal at these levels. This approach helps in locking in gains or minimizing losses during a downtrend.
**Chart Example:**
Here you can see how the price reacted getting closer to this level. All green circles show a bounce-off. So just from looking at the chart we can see a potential bounce again pretty soon.
** Disclosure :**
- ** Performance Claims :** The `Uptrick: Logarithmic Crypto Bands` indicator is designed to assist traders in analyzing price levels and trends. It is important to understand that this tool provides historical data analysis and does not guarantee future performance. The features and benefits described are based on historical market behavior and should not be seen as a prediction of future results. Traders should use this indicator as part of a broader trading strategy and consider other factors before making trading decisions.
Premarket Std Dev BandsOverview
The Premarket Std Dev Bands indicator is a powerful Pine Script tool designed to help traders gain deeper insights into the premarket session's price movements. This indicator calculates and displays the standard deviation bands for premarket trading, providing valuable information on price volatility and potential support and resistance levels during the premarket hours.
Key Features
Premarket Focus: Specifically designed to analyze price movements during the premarket session, offering unique insights not available with traditional indicators.
Customizable Length: Users can adjust the averaging period for calculating the standard deviation, allowing for tailored analysis based on their trading strategy.
Standard Deviation Bands: Displays both 1 and 2 standard deviation bands, helping traders identify significant price movements and potential reversal points.
Real-Time Updates: Continuously updates the premarket open and close prices, ensuring the bands are accurate and reflective of current market conditions.
How It Works
Premarket Session Identification: The script identifies when the current bar is within the premarket session.
Track Premarket Prices: It tracks the open and close prices during the premarket session.
Calculate Premarket Moves: Once the premarket session ends, it calculates the price movement and stores it in an array.
Compute Averages and Standard Deviation: The script calculates the simple moving average (SMA) and standard deviation of the premarket moves over a specified period.
Plot Standard Deviation Bands: Based on the calculated standard deviation, it plots the 1 and 2 standard deviation bands around the premarket open price.
Usage
To utilize the Premarket Std Dev Bands indicator:
Add the script to your TradingView chart.
Adjust the Length input to set the averaging period for calculating the standard deviation.
Observe the plotted standard deviation bands during the premarket session to identify potential trading opportunities.
Benefits
Enhanced Volatility Analysis: Understand price volatility during the premarket session, which can be crucial for making informed trading decisions.
Support and Resistance Levels: Use the standard deviation bands to identify key support and resistance levels, aiding in better entry and exit points.
Customizable and Flexible: Tailor the averaging period to match your trading style and strategy, making this indicator versatile for various market conditions.
Bollinger Bands Liquidity Cloud [ChartPrime]This indicator overlays a heatmap on the price chart, providing a detailed representation of Bollinger bands' profile. It offers insights into the price's behavior relative to these bands. There are two visualization styles to choose from: the Volume Profile and the Z-Score method.
Features
Volume Profile: This method illustrates how the price interacts with the Bollinger bands based on the traded volume.
Z-Score: In this mode, the indicator samples the real distribution of Z-Scores within a specified window and rescales this distribution to the desired sample size. It then maps the distribution as a heatmap by calculating the corresponding price for each Z-Score sample and representing its weight via color and transparency.
Parameters
Length: The period for the simple moving average that forms the base for the Bollinger bands.
Multiplier: The number of standard deviations from the moving average to plot the upper and lower Bollinger bands.
Main:
Style: Choose between "Volume" and "Z-Score" visual styles.
Sample Size: The size of the bin. Affects the granularity of the heatmap.
Window Size: The lookback window for calculating the heatmap. When set to Z-Score, a value of `0` implies using all available data. It's advisable to either use `0` or the highest practical value when using the Z-Score method.
Lookback: The amount of historical data you want the heatmap to represent on the chart.
Smoothing: Implements sinc smoothing to the distribution. It smoothens out the heatmap to provide a clearer visual representation.
Heat Map Alpha: Controls the transparency of the heatmap. A higher value makes it more opaque, while a lower value makes it more transparent.
Weight Score Overlay: A toggle that, when enabled, displays a letter score (`S`, `A`, `B`, `C`, `D`) inside the heatmap boxes, based on the weight of each data point. The scoring system categorizes each weight into one of these letters using the provided percentile ranks and the median.
Color
Color: Color for high values.
Standard Deviation Color: Color to represent the standard deviation on the Bollinger bands.
Text Color: Determines the color of the letter score inside the heatmap boxes. Adjusting this parameter ensures that the score is visible against the heatmap color.
Usage
Once this indicator is applied to your chart, the heatmap will be overlaid on the price chart, providing a visual representation of the price's behavior in relation to the Bollinger bands. The intensity of the heatmap is directly tied to the price action's intensity, defined by your chosen parameters.
When employing the Volume Profile style, a brighter and more intense area on the heatmap indicates a higher trading volume within that specific price range. On the other hand, if you opt for the Z-Score method, the intensity of the heatmap reflects the Z-Score distribution. Here, a stronger intensity is synonymous with a more frequent occurrence of a specific Z-Score.
For those seeking an added layer of granularity, there's the "Weight Score Overlay" feature. When activated, each box in your heatmap will sport a letter score, ranging from `S` to `D`. This score categorizes the weight of each data point, offering a concise breakdown:
- `S`: Data points with a weight of 1.
- `A`: Weights below 1 but greater than or equal to the 75th percentile rank.
- `B`: Weights under the 75th percentile but at or above the median.
- `C`: Weights beneath the median but surpassing the 25th percentile rank.
- `D`: All that fall below the 25th percentile rank.
This scoring feature augments the heatmap's visual data, facilitating a quicker interpretation of the weight distribution across the dataset.
Further Explanations
Volume Profile
A volume profile is a tool used by traders to visualize the amount of trading volume occurring at specific price levels. This kind of profile provides a deep insight into the market's structure and helps traders identify key areas of support and resistance, based on where the most trading activity took place. The concept behind the volume profile is that the amount of volume at each price level can indicate the potential importance of that price.
In this indicator:
- The volume profile mode creates a visual representation by sampling trading volumes across price levels.
- The representation displays the balance between bullish and bearish volumes at each level, which is further differentiated using a color gradient from `low_color` to `high_color`.
- The volume profile becomes more refined with sinc smoothing, helping to produce a smoother distribution of volumes.
Z-Score and Distribution Resampling
Z-Score, in the context of trading, represents the number of standard deviations a data point (e.g., closing price) is from the mean (average). It’s a measure of how unusual or typical a particular data point is in relation to all the data. In simpler terms, a high Z-Score indicates that the data point is far away from the mean, while a low Z-Score suggests it's close to the mean.
The unique feature of this indicator is that it samples the real distribution of z-scores within a window and then resamples this distribution to fit the desired sample size. This process is termed as "resampling in the context of distribution sampling" . Resampling provides a way to reconstruct and potentially simplify the original distribution of z-scores, making it easier for traders to interpret.
In this indicator:
- Each Z-Score corresponds to a price value on the chart.
- The resampled distribution is then used to display the heatmap, with each Z-Score related price level getting a heatmap box. The weight (or importance) of each box is represented as a combination of color and transparency.
How to Interpret the Z-Score Distribution Visualization:
When interpreting the Z-Score distribution through color and alpha in the visualization, it's vital to understand that you're seeing a representation of how unusual or typical certain data points are without directly viewing the numerical Z-Score values. Here's how you can interpret it:
Intensity of Color: This often corresponds to the distance a particular data point is from the mean.
Lighter shades (closer to `low_color`) typically indicate data points that are more extreme, suggesting overbought or oversold conditions. These could signify potential reversals or significant deviations from the norm.
Darker shades (closer to `high_color`) represent data points closer to the mean, suggesting that the price is relatively typical compared to the historical data within the given window.
Alpha (Transparency): The degree of transparency can indicate the significance or confidence of the observed deviation. More opaque boxes might suggest a stronger or more reliable deviation from the mean, implying that the observed behavior is less likely to be a random occurrence.
More transparent boxes could denote less certainty or a weaker deviation, meaning that the observed price behavior might not be as noteworthy.
- Combining Color and Alpha: By observing both the intensity of color and the level of transparency, you get a richer understanding. For example:
- A light, opaque box could suggest a strong, significant deviation from the mean, potentially signaling an overbought or oversold scenario.
- A dark, transparent box might indicate a weak, insignificant deviation, suggesting the price is behaving typically and is close to its average.
Bollinger Bands Delta Matrix Analytics [BDMA] Bollinger Bands Delta Matrix Analytics (BDMA) v7.0
Deep Kinetic Engine – 5x8 Volatility & Delta Decision Matrix
1. Introduction & Concept
Bollinger Bands Delta Matrix Analytics (BDMA) v7.0 is an analytical framework that merges:
- Spatial analysis via Bollinger Bands (%B location),
- with a 4-factor Deep Kinetic Engine based on:
• Total Volume
• Buy Volume
• Sell Volume
• Delta (Buy – Sell) Z-Scores
and converts them into an expanded 5×8 decision matrix that continuously tracks where price is trading and how the underlying orderflow is behaving.
BDMA is not a trading system or strategy. It does not generate entry/exit signals.
Instead, it provides a structured contextual map of volatility, volume, and delta so traders can:
- identify climactic extensions vs. fakeouts,
- distinguish strong initiative moves vs. passive absorption,
- and detect squeezes, traps, and liquidity voids with a unified visual dashboard.
2. Spatial Engine – Bollinger S-States (S1–S5)
The spatial dimension of BDMA comes from classic Bollinger Bands.
Price location is expressed as Percent B (%B) and mapped into 5 spatial states (S-States):
S1 – Hyper Extension (Above Upper Band)
Price has pushed beyond the upper Bollinger Band.
Often associated with parabolic or blow-off behavior, late-stage momentum, and elevated reversal risk.
S2 – Resistance Test (Upper Zone)
Price trades in the upper Bollinger region but remains inside the bands.
Represents a sustained test of resistance, typically within an established or emerging uptrend.
S3 – Neutral Zone (Middle)
Price hovers around the mid-band.
This is the mean reversion gravity field where the market often consolidates or transitions between regimes.
S4 – Support Test (Lower Zone)
Price trades in the lower Bollinger region but inside the bands.
Represents a sustained test of support within range or downtrend structures.
S5 – Hyper Drop (Below Lower Band)
Price extends below the lower Bollinger Band.
Often aligned with panic, forced liquidations, or capitulation-type behavior, with increased snap-back risk.
These 5 S-States define the vertical axis (rows) of the BDMA matrix.
3. Deep Kinetic Engine – 4-Factor Z-Score & D-States (D1–D8)
The Deep Kinetic Engine transforms raw volume and delta into standardized Z-Scores to measure how abnormal current activity is relative to its recent history.
For each bar:
- Raw Buy Volume is estimated from the candle’s position within its range
- Raw Sell Volume is complementary to buy volume
- Raw Delta = Buy Volume – Sell Volume
- Total Volume = Buy Volume + Sell Volume
These 4 series are then normalized using a unified Z-Score lookback to produce:
1. Z_Vol_Total – overall activity and liquidity intensity
2. Z_Vol_Buy – aggression from buyers (attack)
3. Z_Vol_Sell – aggression from sellers (defense or attack)
4. Z_Delta – net victory of one side over the other
Thresholds for Extreme, Significant, and Neutral Z-Score levels are fully configurable, allowing you to tune the sensitivity of the kinetic states.
Using Z_Vol_Total and Z_Delta (plus threshold logic), BDMA assigns one of 8 Deep Kinetic states (D-States):
D1 – Climax Buy
Extreme Total Volume + Extreme Positive Delta → Buying climax or blow-off behavior.
D2 – Strong Buy
High Volume + High Positive Delta → Confirmed bullish initiative activity.
D3 – Weak Buy / Fakeout
Low Volume + High Positive Delta → Bullish delta without commitment, low-liquidity breakout risk.
D4 – Absorption / Conflict
High Volume + Neutral Delta → Aggressive two-way trade, strong absorption, war zone behavior.
D5 – Neutral
Low Volume + Neutral Delta → Low-energy environment with low conviction.
D6 – Weak Sell / Fakeout
Low Volume + High Negative Delta → Bearish delta without commitment, low-liquidity breakdown risk.
D7 – Strong Sell
High Volume + High Negative Delta → Confirmed bearish initiative activity.
D8 – Capitulation
Extreme Volume + Extreme Negative Delta → Panic selling or capitulation regime.
These 8 D-States define the horizontal axis (columns) of the BDMA matrix.
4. The 5×8 BDMA Decision Matrix
The core of BDMA is a 5×8 matrix where:
- Rows (1–5) = Spatial S-States (S1…S5)
- Columns (1–8) = Kinetic D-States (D1…D8)
Each of the 40 possible combinations (SxDy) is pre-computed and mapped to:
- a Status or Regime Title (for example: Climax Breakout, Bear Trap Spring, Capitulation Breakdown),
- a Bias (Climactic Bull, Neutral, Strong Bear, Conflict or Reversal Risk, and similar labels),
- and a Strategic Signal or Consideration (for example: High reversal risk, Wait for confirmation, Low probability zone – avoid).
Internally, BDMA resolves all 40 regimes so the current state can be displayed on the dashboard without performance overhead.
5. Key Regime Families (How to Read the Matrix)
5.1. Breakouts and Breakdowns
Climax Breakout (Top-side)
Spatial S1 with Kinetic D1 or D2
Bias: Explosive or Extreme Bull
Signal:
- Strong or climactic upside extension with abnormal bullish orderflow.
- Trend continuation is possible, but reversal risk is extremely high after blow-off phases.
Low-Conviction Breakout (Fakeout Risk)
S1 with D3 (Weak Buy, low liquidity)
Bias: Weak Bull – Caution
Signal:
- Breakout not supported by volume.
- Elevated risk of failed auction or bull trap.
Capitulation Breakdown (Bottom-side)
Spatial S5 with Kinetic D8
Bias: Climactic Bear (panic)
Signal:
- Capitulation-type selling or forced liquidations.
- Trend can still proceed, but snap-back or violent short-covering risk is high.
Initiative Breakdown vs. Weak Breakdown
- Strong, high-volume breakdown typically corresponds to D7 (Strong Sell).
- Low-volume breakdown often corresponds to D6 (Weak Sell or Fakeout) with potential for failure.
5.2. Absorption, Traps and Springs
Absorption at Resistance (Top-side conflict)
S1 or S2 with D4 (Absorption or Conflict)
Bias: Conflict – Extreme Tension
Signal:
- Heavy two-way trade near resistance.
- Potential distribution or reversal if sellers begin to dominate.
Bull Trap or Failed Auction
Typically S1 with D6 (Weak Sell breakdown behavior after a top-side attempt)
Indicates a breakout attempt that fails and reverses, often after poor liquidity structure.
Absorption at Support and Bear Trap (Spring)
S4 or S5 with D4 or D3
Bias: Conflict or Weak Bear – Reversal Risk
Signal:
- Aggressive buying into lows (spring or shakeout behavior).
- Potential bear trap if price reclaims lost territory.
5.3. Trend Phases
Strong Uptrend Phases
Typically seen when S2–S3 combine with strong bullish kinetic behavior.
Bias: Strong or Extreme Bull
Signal:
- Pullbacks into S3 or S4 with supportive kinetic states often act as trend continuation zones.
Strong Downtrend Phases
Typically seen when S3–S4 combine with strong bearish kinetic behavior.
Bias: Strong or Extreme Bear
Signal:
- Rallies into resistance with strong bearish kinetic backing may act as continuation sell zones.
5.4. Neutral, Exhaustion and Squeeze
Exhaustion or Liquidity Void
S1 or S5 with D5 (Neutral kinetics)
Bias: Neutral or Exhaustion
Signal:
- Spatial extremes without kinetic confirmation.
- Often marks the end of a move, with poor follow-through.
Choppy, Low-Activity Range
S3 with D5
Bias: Neutral
Signal:
- Low volume, low conviction market.
- Typically a low-probability environment where standing aside can be logical.
Squeeze or High-Tension Zone
S3 with D4 or tightly clustered kinetic values
Bias: Conflict or High Tension
Signal:
- Hidden battle inside a volatility contraction.
- Often precedes large directionally-biased moves.
6. Dashboard Layout & Reading Guide
When Show Dashboard is enabled, BDMA displays:
1. Title and Status Line
Name of the current regime (for example: Climax Breakout, Bear Trap Spring, Mean Reversion).
2. Bias Line
Plain-language summary of directional context such as Climactic Bull, Strong Bear, Neutral, or Conflict and Reversal Risk.
3. Signal or Strategic Notes
Concise guidance focused on risk and context, not entries. For example:
- High reversal risk – aggressive traders only
- Wait for confirmation (break or rejection)
- Low probability zone – avoid taking new positions
4. Kinetic Profile (4-Factor Z-Score)
Shows the current Z-Scores for Total Volume (Activity), Buy Volume (Attack), Sell Volume (Defense), and Delta (Net Result).
5. Matrix Heatmap (5×8)
Visual representation of S-State vs. D-State with color coding:
- Bullish clusters in a green spectrum
- Bearish clusters in a red spectrum
- Conflict or exhaustion zones in yellow, amber, or neutral tones
The dashboard can be repositioned (top right, middle right, or bottom right) and its size can be adjusted (Tiny, Small, Normal, or Large) to fit different layouts.
7. Inputs & Customization
7.1. Core Parameters (Bollinger and Z-Score)
- Bollinger Length and Standard Deviation define the spatial engine.
- Z-Score Lookback (All Factors) defines how many bars are used to normalize volume and delta.
7.2. Deep Kinetic Thresholds
- Extreme Threshold defines what is considered climactic (D1 or D8).
- Significant Threshold distinguishes strong initiative vs. weak or fakeout behavior.
- Neutral Threshold is the band within which delta is treated as neutral.
These thresholds allow you to tune the sensitivity of the kinetic classification to fit different timeframes or instruments.
7.3. Calculation Method (Volume Delta)
Geometry (Approx)
- Fast, non-repainting approach based on candle geometry.
- Suitable for most users and real-time decision-making.
Intrabar (Precise)
- Uses lower-timeframe data for more precise volume delta estimation.
- Intrabar mode can repaint and requires compatible data and plan support on the platform.
- Best used for post-analysis or research, not blind automation.
7.4. Visuals and Interface
- Toggle Bollinger Bands visibility on or off.
- Switch between Dark and Light color themes.
- Configure dashboard visibility, matrix heatmap display, position, and size.
8. Multi-Language Semantic Engine (Asia and Middle East Focus)
BDMA v7.0 includes a fully integrated multi-language layer, targeting a wide geographic user base.
Supported Languages:
English, Türkçe, Русский, 简体中文, हिन्दी, العربية, فارسی, עברית
All dashboard labels, regime titles, bias descriptions, and signal texts are dynamically translated via an internal dictionary, while semantic meaning is kept consistent across languages.
This makes BDMA suitable for multi-language communities, study groups, and educational content across different regions.
However, due to the heavy computational load of the Deep Kinetic Engine and TradingView’s strict Pine Script execution limits, it was not possible to expand support to additional languages. Adding more translation layers would significantly increase memory usage and exceed runtime constraints. For this reason, the current language set represents the maximum optimized configuration achievable without compromising performance or stability.
9. Practical Usage Notes
BDMA is most powerful when used as a contextual overlay on top of market structure (HH, HL, LH, LL), higher-timeframe trend, key levels, and your own execution framework.
Recommended usage:
- Identify the current regime (Status and Bias).
- Check whether price location (S-State) and kinetic behavior (D-State) agree with your trade idea.
- Be especially cautious in climactic and absorption or conflict zones, where volatility and risk can be elevated.
Avoid treating BDMA as an automatic green equals buy, red equals sell tool.
The real edge comes from understanding where you are in the volatility or kinetic spectrum, not from forcing signals out of the matrix.
10. Limitations & Important Warnings
BDMA does not predict the future.
It organizes current and recent data into a structured context.
Volume data quality depends on the underlying symbol, exchange, and broker feed.
Forex, crypto, indices, and stocks may all behave differently.
Intrabar mode can repaint and is sensitive to lower-timeframe data availability and your plan type.
Use it with extra caution and primarily for research.
No indicator can remove the need for clear trading rules, disciplined risk management, and psychological control.
11. Disclaimer
This script is provided strictly for educational and analytical purposes.
It is not a trading system, signal service, financial product, or investment advice.
Nothing in this indicator or its description should be interpreted as a recommendation to buy or sell any asset.
Past behavior of any indicator or market pattern does not guarantee future results.
Trading and investing involve significant risk, including the risk of losing more than your initial capital in leveraged products.
You are solely responsible for your own decisions, risk management, and results.
By using this script, you acknowledge that you understand these risks and agree that the author or authors and publisher or publishers are not liable for any loss or damage arising from its use.
VWAP Bands [UAlgo]The "VWAP Bands " indicator is designed to provide traders with valuable insights into market trends and potential support/resistance levels using Volume Weighted Average Price (VWAP) bands. This indicator integrates the core concepts of VWAP with additional trend analysis features, making it a versatile tool for both range trading and trend-following strategies.
The VWAP bands are plotted based on the standard deviation multipliers, creating upper and lower bands around the VWAP. These bands serve as dynamic support and resistance levels. When the price approaches these bands, traders can anticipate potential reversals or continuations of the current trend. Additionally, the indicator provides visual cues for trend strength and potential trend changes, helping traders make informed decisions in various market conditions.
🔶 Settings
Source (Data Source): The data source for VWAP calculations. The default setting is the typical price (HLC3), which is the average of the high, low, and close prices.
Length: The number of bars used in the VWAP calculation. This determines the lookback period for the indicator.
Standard Deviation Multiplier: The multiplier applied to the standard deviation to create the primary upper and lower VWAP bands. This setting controls the distance of the bands from the VWAP.
Secondary Standard Deviation Multiplier: The multiplier applied to the standard deviation to create the secondary upper and lower VWAP bands, providing additional levels of support and resistance.
Display Trend: A toggle to enable or disable the display of the trend analysis feature. When enabled, the indicator highlights trend strength and potential trend changes.
Display Trend Crossovers: A toggle to enable or disable the display of trend crossover signals. When enabled, the indicator plots shapes to indicate where trend switches are likely occurring.
🔶 Calculations
The calculations behind the "VWAP Bands " indicator begin with determining the Volume Weighted Average Price (VWAP), which provides a comprehensive view of the average price of an asset, weighted by trading volume. This gives a more accurate representation of the asset's true average price over a specified period.
The first step in this process involves summing the trading volume over a chosen period, typically represented by the length parameter. Simultaneously, the product of the price (usually an average of the high, low, and close prices) and the trading volume is calculated and summed. By dividing this cumulative price-volume product by the total volume, we obtain the VWAP value. This VWAP serves as the central anchor around which the price action oscillates.
To enhance the utility of VWAP, we introduce standard deviation calculations. Standard deviation measures the extent of price dispersion from the VWAP, providing insight into price volatility. By calculating the variance (which involves the squared deviations of price) and then taking its square root, we derive the standard deviation. This helps in understanding how far prices typically stray from the VWAP.
With the VWAP and standard deviation in hand, we then establish upper and lower bands by adding and subtracting multiples of the standard deviation from the VWAP. These bands act as dynamic support and resistance levels, adapting to changes in market volatility. The primary bands, set by the first standard deviation multiplier, are augmented by secondary bands defined by a larger multiplier, offering additional layers of potential support and resistance.
It also integrates trend analysis, highlighting areas where the price action suggests a strong or weak trend. This is achieved by overlaying colored zones above and below the bands, indicating the strength and direction of the trend. When the price crosses these bands, it signals potential trend changes, aiding traders in making timely decisions.
🔶 Disclaimer
The "VWAP Bands " indicator is provided for educational and informational purposes only. It is not intended as financial advice and should not be construed as such.
Trading involves significant risk and may not be suitable for all investors. Before using this indicator or making any investment decisions, it is important to conduct thorough research and consider your financial situation.
Bollinger Bands (Nadaraya Smoothed) | Flux ChartsTicker: AMEX:SPY , Timeframe: 1m, Indicator settings: default
General Purpose
This script is an upgrade to the classic Bollinger Bands. The idea behind Bollinger bands is the detection of price movements outside of a stock's typical fluctuations. Bollinger Bands use a moving average over period n plus/minus the standard deviation over period n times a multiplier. When price closes above or below either band this can be considered an abnormal movement. This script allows for the classic Bollinger Band interpretation while de-noising or "smoothing" the bands.
Efficacy
Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: Standard Dev: 2; Level 1 : off; Level 2: off; labels: off
Upper Band Key:
Blue: Bollinger No smoothing
Orange: Bollinger SMA smoothing period of 10
Purple: Bollinger EMA smoothing period of 10
Red: Nadaraya Smoothed Bollinger bandwidth of 6
Here we chose periods so that each would have a similar offset from the original Bollinger's. Notice that the Red Band has a much smoother result while on average having a similar fit to the other smoothing techniques. Increasing the EMA's or SMA's period would result in them being smoother however the offset would increase making them less accurate to the original data.
Ticker: AMEX:SPY , Timeframe: 1m, Indicator settings: Standard Dev: 2; Level 1: off; Level 2: off; labels: off
Upper Band Key:
Blue: Bollinger No smoothing
Orange: Bollinger SMA smoothing period of 20
Purple: Bollinger EMA smoothing period of 20
Red: Nadaraya Smoothed Bollinger bandwidth of 6
This makes the Nadaraya estimator a particularly efficacious technique in this use case as it achieves a superior smoothness to fit ratio.
How to Use
This indicator is not intended to be used on its own. Its use case is to identify outlier movements and periods of consolidation. The Smoothing Factor when lowered results in a more reactive but noisy graph. This setting is also known as the "bandwidth" ; it essentially raises the amplitude of the kernel function causing a greater weighting to recent data similar to lowering the period of a SMA or EMA. The repaint smoothing simply draws on the Bollinger's each chart update. Typically repaint would be used for processing and displaying discrete data however currently it's simply another way to display the Bollinger Bands.
What makes this script unique.
Since Bollinger bands use standard deviation they have excess noise. By noise we mean minute fluctuations which most traders will not find useful in their strategies. The Nadaraya-Watson estimator, as used, is essentially a weighted average akin to an ema. A gaussian kernel is placed at the candlestick of interest. That candlestick's value will have the highest weight. From that point the other candlesticks' values effect on the average will decrease with the slope of the kernel function. This creates a localized mean of the Bollinger Bands allowing for reduced noise with minimal distortion of the original Bollinger data.
Rolling Range Bands by tvigRolling Range Bands
Plots two dynamic price envelopes that track the highest and lowest prices over a Short and Long lookback. Use them to see near-term vs. broader market structure, evolving support/resistance, and volatility changes at a glance.
What it shows
• Short Bands: recent trading range (fast, more reactive).
• Long Bands: broader range (slow, structural).
• Optional step-line style and shaded zones for clarity.
• Option to use completed bar values to avoid intrabar jitter (no repaint).
How to read
• Price pressing the short high while the long band rises → short-term momentum in a larger uptrend.
• Price riding the short low inside a falling long band → weakness with trend alignment.
• Band squeeze (narrowing) → compression; watch for breakout.
• Band expansion (widening) → rising volatility; expect larger swings.
• Repeated touches/rejections of long bands → potential areas of support/resistance.
Inputs
• Short Window, Long Window (bars)
• Use Close only (vs. High/Low)
• Use completed bar values (stability)
• Step-line style and Band shading
Tips
• Works on any symbol/timeframe; tune windows to your market.
• For consistent scaling, pin the indicator to the same right price scale as the chart.
Not financial advice; combine with trend/volume/RSI or your system for entries/exits.
[MAD] Fibonacci Bands with SmoothingHi, this is just an easy script, nothing special, it was a request from a community member and was finished in just 40 minutes :D
This indicator offers a approach to tracking market price movements by utilizing Fibonacci-based levels combined with customizable smoothing options for both the bands and the high/low values.
Key Features:
Customizable Moving Averages: Choose from a variety of smoothing methods, including SMA, EMA, WMA, HMA, VWMA, and advanced Ehlers-based methods.
This allows for flexible adaptation to different assets.
Multiple Fibonacci Band Multipliers: The user can define six different multipliers for both the upper and lower Fibonacci bands, allowing for granular customization of the indicator. The middle line serves as the central reference, and the multipliers extend the bands outward based on price range dynamics.
High/Low Smoothing: In addition to smoothing the Fibonacci bands, users can apply smoothing to the high and low prices that form the basis for calculating the Fibonacci bands. This ensures that the indicator responds smoothly to market movements, reducing noise while capturing key trends.
Forward Shift Option: Allows for projecting the bands into the future by shifting the calculated levels forward by a user-specified number of periods. This feature is particularly useful for those interested in anticipating price actions and future trends.
Visual Enhancements: The indicator features filled regions between bands to clearly visualize the zones of price movement. The fills between the bands offer insight into potential support and resistance zones, based on price levels defined by the Fibonacci ratios.
How It Works:
The indicator uses the highest and lowest closing prices over a specified lookback period to establish a price range. Based on this range, it calculates the middle line (0.5 level) and applies user-defined Fibonacci multipliers to generate both upper and lower bands. Users have control over the smoothing method for both the high/low prices and the bands themselves, allowing for an adaptive experience that can be tailored to different timeframes or market conditions.
For visualization, areas between the upper and lower bands are filled with distinct colors, providing an intuitive view of the potential price zones where the market might react or consolidate.
These fills highlight the zones created by the Fibonacci bands, helping users identify critical market levels with ease.
have fun
p.s.: @frankchef hope that suits your needs & expectations ;-)
Sinc Bollinger BandsKaiser Windowed Sinc Bollinger Bands Indicator
The Kaiser Windowed Sinc Bollinger Bands indicator combines the advanced filtering capabilities of the Kaiser Windowed Sinc Moving Average with the volatility measurement of Bollinger Bands. This indicator represents a sophisticated approach to trend identification and volatility analysis in financial markets.
Core Components
At the heart of this indicator is the Kaiser Windowed Sinc Moving Average, which utilizes the sinc function as an ideal low-pass filter, windowed by the Kaiser function. This combination allows for precise control over the frequency response of the moving average, effectively separating trend from noise in price data.
The sinc function, representing an ideal low-pass filter, provides the foundation for the moving average calculation. By using the sinc function, analysts can independently control two critical parameters: the cutoff frequency and the number of samples used. The cutoff frequency determines which price movements are considered significant (low frequency) and which are treated as noise (high frequency). The number of samples influences the filter's accuracy and steepness, allowing for a more precise approximation of the ideal low-pass filter without altering its fundamental frequency response characteristics.
The Kaiser window is applied to the sinc function to create a practical, finite-length filter while minimizing unwanted oscillations in the frequency domain. The alpha parameter of the Kaiser window allows users to fine-tune the trade-off between the main-lobe width and side-lobe levels in the frequency response.
Bollinger Bands Implementation
Building upon the Kaiser Windowed Sinc Moving Average, this indicator adds Bollinger Bands to provide a measure of price volatility. The bands are calculated by adding and subtracting a multiple of the standard deviation from the moving average.
Advanced Centered Standard Deviation Calculation
A unique feature of this indicator is its specialized standard deviation calculation for the centered mode. This method employs the Kaiser window to create a smooth deviation that serves as an highly effective envelope, even though it's always based on past data.
The centered standard deviation calculation works as follows:
It determines the effective sample size of the Kaiser window.
The window size is then adjusted to reflect the target sample size.
The source data is offset in the calculation to allow for proper centering.
This approach results in a highly accurate and smooth volatility estimation. The centered standard deviation provides a more refined and responsive measure of price volatility compared to traditional methods, particularly useful for historical analysis and backtesting.
Operational Modes
The indicator offers two operational modes:
Non-Centered (Real-time) Mode: Uses half of the windowed sinc function and a traditional standard deviation calculation. This mode is suitable for real-time analysis and current market conditions.
Centered Mode: Utilizes the full windowed sinc function and the specialized Kaiser window-based standard deviation calculation. While this mode introduces a delay, it offers the most accurate trend and volatility identification for historical analysis.
Customizable Parameters
The Kaiser Windowed Sinc Bollinger Bands indicator provides several key parameters for customization:
Cutoff: Controls the filter's cutoff frequency, determining the divide between trends and noise.
Number of Samples: Sets the number of samples used in the FIR filter calculation, affecting the filter's accuracy and computational complexity.
Alpha: Influences the shape of the Kaiser window, allowing for fine-tuning of the filter's frequency response characteristics.
Standard Deviation Length: Determines the period over which volatility is calculated.
Multiplier: Sets the number of standard deviations used for the Bollinger Bands.
Centered Alpha: Specific to the centered mode, this parameter affects the Kaiser window used in the specialized standard deviation calculation.
Visualization Features
To enhance the analytical value of the indicator, several visualization options are included:
Gradient Coloring: Offers a range of color schemes to represent trend direction and strength for the moving average line.
Glow Effect: An optional visual enhancement for improved line visibility.
Background Fill: Highlights the area between the Bollinger Bands, aiding in volatility visualization.
Applications in Technical Analysis
The Kaiser Windowed Sinc Bollinger Bands indicator is particularly useful for:
Precise trend identification with reduced noise influence
Advanced volatility analysis, especially in the centered mode
Identifying potential overbought and oversold conditions
Recognizing periods of price consolidation and potential breakouts
Compared to traditional Bollinger Bands, this indicator offers superior frequency response characteristics in its moving average and a more refined volatility measurement, especially in centered mode. These features allow for a more nuanced analysis of price trends and volatility patterns across various market conditions and timeframes.
Conclusion
The Kaiser Windowed Sinc Bollinger Bands indicator represents a significant advancement in technical analysis tools. By combining the ideal low-pass filter characteristics of the sinc function, the practical benefits of Kaiser windowing, and an innovative approach to volatility measurement, this indicator provides traders and analysts with a sophisticated instrument for examining price trends and market volatility.
Its implementation in Pine Script contributes to the TradingView community by making advanced signal processing and statistical techniques accessible for experimentation and further development in technical analysis. This indicator serves not only as a practical tool for market analysis but also as an educational resource for those interested in the intersection of signal processing, statistics, and financial markets.
Related:
Volatility ATR Support and Resistance Bands [Quantigenics]Volatility ATR Support and Resistance Bands
The “Volatility ATR Support and Resistance Bands” is a trend visualization tool that uses Average True Range (ATR) to create a dynamic channel around price action, adapting to changes in volatility and offering clear trend indicators. The band direction can indicate trend and the lines can indicate support and resistance levels.
The script works by calculating a series of moving averages from the highest and lowest prices, then applies an ATR-based multiplier to generate a set of bands. These bands expand and contract with the market’s volatility, providing a visual guide to the strength and potential direction of price movements.
How to Trade with Volatility ATR Band:
Identify Trend Direction: When the bands slope upwards, the market is trending upwards, which may be a good opportunity to consider a long position. When the bands slope downward, the market is trending downwards, which could be a sign to sell or short.
Volatility Awareness: The wider the bands, the higher the market volatility. Narrow bands suggest a quieter market, which might indicate consolidation or a potential breakout/breakdown.
Confirm Entries and Exits: Use the bands as dynamic support and resistance; entering trades as the price bounces off the bands and considering exits as it reaches the opposite side or breaches the bands.
Hope you enjoy this script!
Happy trading!
ATR Bands with Optional Risk/Reward Colors█ OVERVIEW
This indicator projects ATR bands and, optionally, colors them based on a risk/reward advantage for those who trade breakouts/breakdowns using moving averages as partial or full exit points.
█ DEFINITIONS
► True Range
The True Range is a measure of the volatility of a financial asset and is defined as the maximum difference among one of the following values:
- The high of the current period minus the low of the current period.
- The absolute value of the high of the current period minus the closing price of the previous period.
- The absolute value of the low of the current period minus the closing price of the previous period.
► Average True Range
The Average True Range was developed by J. Welles Wilder Jr. and was introduced in his 1978 book titled "New Concepts in Technical Trading Systems". It is calculated as an average of the true range values over a certain number of periods (usually 14) and is commonly used to measure volatility and set stop-loss and profit targets (1).
For example, if you are looking at a daily chart and you want to calculate the 14-day ATR, you would take the True Range of the previous 14 days, calculate their average, and this would be the ATR for that day. The process is then repeated every day to obtain a series of ATR values over time.
The ATR can be smoothed using different methods, such as the Simple Moving Average (SMA), the Exponential Moving Average (EMA), or others, depending on the user's preferences or analysis needs.
► ATR Bands
The ATR bands are created by adding or subtracting the ATR from a reference point (usually the closing price). This process generates bands around the central point that expand and contract based on market volatility, allowing traders to assess dynamic support and resistance levels and to adapt their trading strategies to current market conditions.
█ INDICATOR
► ATR Bands
The indicator provides all the essential parameters for calculating the ATR: period length, time frame, smoothing method, and multiplier.
It is then possible to choose the reference point from which to create the bands. The most commonly used reference points are Open, High, Low, and Close, but you can also choose the commonly used candle averages: HL2, HLC3, HLCC4, OHLC4. Among these, there is also a less common "OC2", which represents the average of the candle body. Additionally, two parameters have been specifically created for this indicator: Open/Close and High/Low.
With the "Open/Close" parameter, the upper band is calculated from the higher value between Open and Close, while the lower one is calculated from the lower value between Open and Close. In the case of bullish candles, therefore, the Close value is taken as the starting point for the upper band and the Open value for the lower one; conversely, in bearish candles, the Open value is used for the upper band and the Close value for the lower band. This setting can be useful for precautionally generating broader bands when trading with candlesticks like hammers or inverted hammers.
The "High/Low" parameter calculates the upper band starting from the High and the lower band starting from the Low. Among all the available options, this one allows drawing the widest bands.
Other possible options to improve the drawing of ATR bands, aligning them with the price action, are:
• Doji Smoothing: When the current candle is a doji (having the same Open and Close price), the bands assume the values they had on the previous candle. This can be useful to avoid steep fluctuations of the bands themselves.
• Extend to High/Low: Extends the bands to the High or Low values when they exceed the value of the band.
• Round Last Cent: Expands the upper band by one cent if the price ends with x.x9, and the lower band if the price ends with x.x1. This function only works when the asset's tick is 0.01.
► Risk/Reward Advantage
The indicator optionally colors the ATR bands after setting a breakpoint, one or two risk/reward ratios, and a series of moving averages. This function allows you to know in advance whether entering a trade can provide an advantage over the risk. The band is colored when the ratio between the distance from the break point to the band and the distance from the break point to the first available moving average reaches at least the set ratio value. It is possible to set two colorings, one for a minimum risk/reward ratio and one for an optimal risk/reward ratio.
The break point can be chosen between High/Low (High in case of breakout, Low in case of breakdown) or Open/Close (on breakouts, Close with bullish candles or Open with bearish candles; on breakdowns, Close with bearish candles or Open with bullish candles).
It is possible to choose up to 10 moving averages of various types, including the VWAP with the Anchor Period (2).
Depending on the "Price to MA" setting, the bands can be individually or simultaneously colored.
By selecting "Single Direction," the risk/reward calculation is performed only when all moving averages are above or below the break point, resulting in only one band being colored at a time. For this reason, when the break point is in between the moving averages, the calculation is not executed. This setting can be useful for strategies involving price movement from a level towards a series of specific moving averages (for example, in reversals starting from a certain level towards the VWAP with possible partial take profits on some previous moving averages, or simply in trend following towards one or more moving averages).
Choosing "Both Directions" the risk/reward ratio is calculated based on the first available moving averages both above and below the price. This setting is useful for those who operate in range bound markets or simply take advantage of movements between moving averages.
█ NOTE
This script may not be suitable for scalping strategies that require immediate entries due to the inability to know the ATR of a candle in advance until its closure. Once the candle is closed, you should have time to place a stop or stop-limit order, so your strategy should not anticipate an immediate start with the next candle. Even more conveniently, if your strategy involves an entry on a pullback, you can place a limit order at the breakout level.
(1) www.tradingview.com
(2) For convenience, the code for the Anchor Period has been entirely copied from the VWAP code provided by TradingView.
Logarithmic Bollinger Bands with Secondary BandThe Logarithmic Bollinger Bands indicator is a variation of standard Bollinger Bands that applies the calculation on a logarithmic scale rather than directly on price values. This approach is particularly useful in markets where assets experience exponential growth or large percentage-based moves, as it allows the bands to adapt proportionally instead of being distorted by absolute price changes. The indicator calculates a moving average of the log-transformed price (the basis), then adds and subtracts multiples of the standard deviation of the log price to form the upper and lower bands. Finally, the results are converted back to normal price scale for plotting. In addition to the primary bands set at 2.0 standard deviations, this version includes a secondary band set at 0.5 standard deviations, offering a closer inner envelope around the mean.
To use this indicator
Traders can observe how price interacts with both the inner and outer bands. The outer 2.0 standard deviation bands represent traditional Bollinger-style boundaries, highlighting potential overbought or oversold conditions when price pushes beyond them.
The inner 0.5 bands provide an earlier signal of price compression and breakout potential, as moves outside these tighter bands often precede larger volatility expansions.
Together, these dual layers give traders a way to monitor both short-term fluctuations and broader trend extremes, making it easier to spot opportunities for entries, exits, or risk management in markets where percentage-based scaling is more meaningful than raw price levels.
Adaptive Rolling Quantile Bands [CHE] Adaptive Rolling Quantile Bands
Part 1 — Mathematics and Algorithmic Design
Purpose. The indicator estimates distribution‐aware price levels from a rolling window and turns them into dynamic “buy” and “sell” bands. It can work on raw price or on *residuals* around a baseline to better isolate deviations from trend. Optionally, the percentile parameter $q$ adapts to volatility via ATR so the bands widen in turbulent regimes and tighten in calm ones. A compact, latched state machine converts these statistical levels into high-quality discretionary signals.
Data pipeline.
1. Choose a source (default `close`; MTF optional via `request.security`).
2. Optionally compute a baseline (`SMA` or `EMA`) of length $L$.
3. Build the *working series*: raw price if residual mode is off; otherwise price minus baseline (if a baseline exists).
4. Maintain a FIFO buffer of the last $N$ values (window length). All quantiles are computed on this buffer.
5. Map the resulting levels back to price space if residual mode is on (i.e., add back the baseline).
6. Smooth levels with a short EMA for readability.
Rolling quantiles.
Given the buffer $X_{t-N+1..t}$ and a percentile $q\in $, the indicator sorts a copy of the buffer ascending and linearly interpolates between adjacent ranks to estimate:
* Buy band $\approx Q(q)$
* Sell band $\approx Q(1-q)$
* Median $Q(0.5)$, plus optional deciles $Q(0.10)$ and $Q(0.90)$
Quantiles are robust to outliers relative to means. The estimator uses only data up to the current bar’s value in the buffer; there is no look-ahead.
Residual transform (optional).
In residual mode, quantiles are computed on $X^{res}_t = \text{price}_t - \text{baseline}_t$. This centers the distribution and often yields more stationary tails. After computing $Q(\cdot)$ on residuals, levels are transformed back to price space by adding the baseline. If `Baseline = None`, residual mode simply falls back to raw price.
Volatility-adaptive percentile.
Let $\text{ATR}_{14}(t)$ be current ATR and $\overline{\text{ATR}}_{100}(t)$ its long SMA. Define a volatility ratio $r = \text{ATR}_{14}/\overline{\text{ATR}}_{100}$. The effective quantile is:
Smoothing.
Each level is optionally smoothed by an EMA of length $k$ for cleaner visuals. This smoothing does not change the underlying quantile logic; it only stabilizes plots and signals.
Latched state machines.
Two three-step processes convert levels into “latched” signals that only fire after confirmation and then reset:
* BUY latch:
(1) HLC3 crosses above the median →
(2) the median is rising →
(3) HLC3 prints above the upper (orange) band → BUY latched.
* SELL latch:
(1) HLC3 crosses below the median →
(2) the median is falling →
(3) HLC3 prints below the lower (teal) band → SELL latched.
Labels are drawn on the latch bar, with a FIFO cap to limit clutter. Alerts are available for both the simple band interactions and the latched events. Use “Once per bar close” to avoid intrabar churn.
MTF behavior and repainting.
MTF sourcing uses `lookahead_off`. Quantiles and baselines are computed from completed data only; however, any *intrabar* cross conditions naturally stabilize at close. As with all real-time indicators, values can update during a live bar; prefer bar-close alerts for reliability.
Complexity and parameters.
Each bar sorts a copy of the $N$-length window (practical $N$ values keep this inexpensive). Typical choices: $N=50$–$100$, $q_0=0.15$–$0.25$, $k=2$–$5$, baseline length $L=20$ (if used), adaptation strength $s=0.2$–$0.7$.
Part 2 — Practical Use for Discretionary/Active Traders
What the bands mean in practice.
The teal “buy” band marks the lower tail of the recent distribution; the orange “sell” band marks the upper tail. The median is your dynamic equilibrium. In residual mode, these tails are deviations around trend; in raw mode they are absolute price percentiles. When ATR adaptation is on, tails breathe with regime shifts.
Two core playbooks.
1. Mean-reversion around a stable median.
* Context: The median is flat or gently sloped; band width is relatively tight; instrument is ranging.
* Entry (long): Look for price to probe or close below the buy band and then reclaim it, especially after HLC3 recrosses the median and the median turns up.
* Stops: Place beyond the most recent swing low or $1.0–1.5\times$ ATR(14) below entry.
* Targets: First scale at the median; optional second scale near the opposite band. Trail with the median or an ATR stop.
* Symmetry: Mirror the rules for shorts near the sell band when the median is flat to down.
2. Continuation with latched confirmations.
* Context: A developing trend where you want fewer but cleaner signals.
* Entry (long): Take the latched BUY (3-step confirmation) on close, or on the next bar if you require bar-close validation.
* Invalidation: A close back below the median (or below the lower band in strong trends) negates momentum.
* Exits: Trail under the median for conservative exits or under the teal band for trend-following exits. Consider scaling at structure (prior swing highs) or at a fixed $R$ multiple.
Parameter guidance by timeframe.
* Scalping / LTF (1–5m): $N=30$–$60$, $q_0=0.20$, $k=2$–3, residual mode on, baseline EMA $L=20$, adaptation $s=0.5$–0.7 to handle micro-vol spikes. Expect more signals; rely on latched logic to filter noise.
* Intraday swing (15–60m): $N=60$–$100$, $q_0=0.15$–0.20, $k=3$–4. Residual mode helps but is optional if the instrument trends cleanly. $s=0.3$–0.6.
* Swing / HTF (4H–D): $N=80$–$150$, $q_0=0.10$–0.18, $k=3$–5. Consider `SMA` baseline for smoother residuals and moderate adaptation $s=0.2$–0.4.
Baseline choice.
Use EMA for responsiveness (fast trend shifts) and SMA for stability (smoother residuals). Turning residual mode on is advantageous when price exhibits persistent drift; turning it off is useful when you explicitly want absolute bands.
How to time entries.
Prefer bar-close validation for both band recaptures and latched signals. If you must act intrabar, accept that crosses can “un-cross” before close; compensate with tighter stops or reduced size.
Risk management.
Position size to a fixed fractional risk per trade (e.g., 0.5–1.0% of equity). Define invalidation using structure (swing points) plus ATR. Avoid chasing when distance to the opposite band is small; reward-to-risk degrades rapidly once you are deep inside the distribution.
Combos and filters.
* Pair with a higher-timeframe median slope as a regime filter (trade only in the direction of the HTF median).
* Use band width relative to ATR as a range/trend gauge: unusually narrow bands suggest compression (mean-reversion bias); expanding bands suggest breakout potential (favor latched continuation).
* Volume or session filters (e.g., avoid illiquid hours) can materially improve execution.
Alerts for discretion.
Enable “Cross above Buy Level” / “Cross below Sell Level” for early notices and “Latched BUY/SELL” for conviction entries. Set alerts to “Once per bar close” to avoid noise.
Common pitfalls.
Do not interpret band touches as automatic signals; context matters. A strong trend will often ride the far band (“band walking”) and punish counter-trend fades—use the median slope and latched logic to separate trend from range. Do not oversmooth levels; you will lag breaks. Do not set $q$ too small or too large; extremes reduce statistical meaning and practical distance for stops.
A concise checklist.
1. Is the median flat (range) or sloped (trend)?
2. Is band width expanding or contracting vs ATR?
3. Are we near the tail level aligned with the intended trade?
4. For continuation: did the 3 steps for a latched signal complete?
5. Do stops and targets produce acceptable $R$ (≥1.5–2.0)?
6. Are you trading during liquid hours for the instrument?
Summary. ARQB provides statistically grounded, regime-aware bands and a disciplined, latched confirmation engine. Use the bands as objective context, the median as your equilibrium line, ATR adaptation to stay calibrated across regimes, and the latched logic to time higher-quality discretionary entries.
Disclaimer
No indicator guarantees profits. Adaptive Rolling Quantile Bands is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence 🚀
Best regards
Chervolino
Fibonacci Enhanced Bollinger BandsDiscover the synergistic power of Fibonacci ratios with traditional Bollinger Bands in the 'Fibonacci Enhanced Bollinger Bands' indicator. Ideal for traders seeking dynamic price levels for strategic entries and exits, this tool adds a unique Fibonacci twist to your technical analysis toolkit.
Introduction to Fibonacci Enhanced Bollinger Bands
'Fibonacci Enhanced Bollinger Bands' is a trading indicator that combines the classic Bollinger Bands approach with the powerful insights of Fibonacci ratios. By integrating these two concepts, this indicator offers traders a unique perspective on market volatility and potential support/resistance levels.
How It Works
Core Concept : The indicator calculates Bollinger Bands using a selected Fibonacci ratio. This ratio is applied to the standard deviation of the price series, providing a dynamic range around a Simple Moving Average (SMA).
Trading Strategies
Long Opportunities : The area below the lower band can be considered a potential zone for long positions. Prices in this zone may indicate an oversold market condition, suggesting a possible reversal or pullback.
Short Opportunities : Conversely, the area above the upper band might signal short-selling opportunities. Prices in this region could imply an overbought scenario, potentially leading to a price decline.
Versatility : Whether you're a day trader, swing trader, or long-term investor, this indicator adapts to various timeframes and assets, making it a versatile tool in your trading arsenal.
Conclusion
The 'Fibonacci Enhanced Bollinger Bands' indicator is designed for traders who wish to leverage the power of Fibonacci ratios in conjunction with the volatility insights provided by Bollinger Bands. It's an excellent tool for identifying potential reversal zones and refining entry and exit points. Try it out to enhance your market analysis and support your trading decisions with the combined wisdom of Fibonacci and Bollinger Bands.
Bollinger Bands Heatmap (BBH)The Bollinger Bands Heatmap (BBH) Indicator provides a unique visualization of Bollinger Bands by displaying the full distribution of prices as a heatmap overlaying your price chart. Unlike traditional Bollinger Bands, which plot the mean and standard deviation as lines, BBH illustrates the entire statistical distribution of prices based on a normal distribution model.
This heatmap indicator offers traders a visually appealing way to understand the probabilities associated with different price levels. The lower the weight of a certain level, the more transparent it appears on the heatmap, making it easier to identify key areas of interest at a glance.
Key Features
Dynamic Heatmap: Changes in real-time as new price data comes in.
Fully Customizable: Adjust the scale, offset, alpha, and other parameters to suit your trading style.
Visually Engaging: Uses gradients of colors to distinguish between high and low probabilities.
Settings
Scale
Tooltip: Scale the size of the heatmap.
Purpose: The 'Scale' setting allows you to adjust the dimensions of each heatmap box. A higher value will result in larger boxes and a more generalized view, while a lower value will make the boxes smaller, offering a more detailed look at price distributions.
Values: You can set this from a minimum of 0.125, stepping up by increments of 0.125.
Scale ATR Length
Tooltip: The ATR used to scale the heatmap boxes.
Purpose: This setting is designed to adapt the heatmap to the instrument's volatility. It determines the length of the Average True Range (ATR) used to size the heatmap boxes.
Values: Minimum allowable value is 5. You can increase this to capture more bars in the ATR calculation for greater smoothing.
Offset
Tooltip: Offset mean by ATR.
Purpose: The 'Offset' setting allows you to shift the mean value by a specified ATR. This could be useful for strategies that aim to capitalize on extreme price movements.
Values: The value can be any floating-point number. Positive values shift the mean upward, while negative values shift it downward.
Multiplier
Tooltip: Bollinger Bands Multiplier.
Purpose: The 'Multiplier' setting determines how wide the Bollinger Bands are around the mean. A higher value will result in a wider heatmap, capturing more extreme price movements. A lower value will tighten the heatmap around the mean price.
Values: The minimum is 0, and you can increase this in steps of 0.2.
Length
Tooltip: Length of Simple Moving Average (SMA).
Purpose: This setting specifies the period for the Simple Moving Average that serves as the basis for the Bollinger Bands. A higher value will produce a smoother average, while a lower value will make it more responsive to price changes.
Values: Can be set to any integer value.
Heat Map Alpha
Tooltip: Opacity level of the heatmap.
Purpose: This controls the transparency of the heatmap. A lower value will make the heatmap more transparent, allowing you to see the price action more clearly. A higher value will make the heatmap more opaque, emphasizing the bands.
Values: Ranges from 0 (completely transparent) to 100 (completely opaque).
Color Settings
High Color & Low Color: These settings allow you to customize the gradient colors of the heatmap.
Purpose: Use contrasting colors for better visibility or colors that you prefer. The 'High Color' is used for areas with high density (high probability), while the 'Low Color' is for low-density areas (low probability).
Usage Scenarios for Settings
For Volatile Markets: Increase 'Scale ATR Length' for better smoothing and set a higher 'Multiplier' to capture wider price movements.
For Trend Following: You might want to set a larger 'Length' for the SMA and adjust 'Scale' and 'Offset' to focus on more probable price zones.
These are just recommendations; feel free to experiment with these settings to suit your specific trading requirements.
How To Interpret
The heatmap gives a visual representation of the range within which prices are likely to move. Areas with high density (brighter color) indicate a higher probability of the price being in that range, whereas areas with low density (more transparent) indicate a lower probability.
Bright Areas: Considered high-probability zones where the price is more likely to be.
Transparent Areas: Considered low-probability zones where the price is less likely to be.
Tips For Use
Trend Confirmation: Use the heatmap along with other trend indicators to confirm the strength and direction of a trend.
Volatility: Use the density and spread of the heatmap as an indication of market volatility.
Entry and Exit: High-density areas could be potential support and resistance levels, aiding in entry and exit decisions.
Caution
The Bollinger Bands Heatmap assumes a normal distribution of prices. While this is a standard assumption in statistics, it is crucial to understand that real-world price movements may not always adhere to a normal distribution.
Conclusion
The Bollinger Bands Heatmap Indicator offers traders a fresh perspective on Bollinger Bands by transforming them into a visual, real-time heatmap. With its customizable settings and visually engaging display, BBH can be a useful tool for traders looking to understand price probabilities in a dynamic way.
Feel free to explore its features and adjust the settings to suit your trading strategy. Happy trading!
Multi-Timeframe Bollinger BandsMy hope is to optimize the settings for this indicator and reintroduce it as a "strategy" with suggested position entry and exit points shown in the price pane.
I’ve been having good results setting the “Bollinger Band MA Length” in the Input tab to between 5 and 10. You can use the standard 20 period, but your results will not be as granular.
This indicator has proven very good at finding local tops and bottoms by combining data from multiple timeframes. Use timeframes that are lower than the timeframe you are viewing in your price pane. Be cognizant that the indicator, like other oscillators, does occasionally produce divergences at tops and bottoms.
Any feedback is appreciated.
Overview
This indicator is an oscillator that measures the normalized position of the price relative to Bollinger Bands across multiple timeframes. It takes the price's position within the Bollinger Bands (calculated on different timeframes) and averages those positions to create a single value that oscillates between 0 and 1. This value is then plotted as the oscillator, with reference lines and colored regions to help interpret the price's relative strength or weakness.
How It Works
Bollinger Band Calculation:
The indicator uses a custom function f_getBBPosition() to calculate the position of the price within Bollinger Bands for a given timeframe.
Price Position Normalization:
For each timeframe, the function normalizes the price's position between the upper and lower Bollinger Bands.
It calculates three positions based on the high, low, and close prices of the requested timeframe:
pos_high = (High - Lower Band) / (Upper Band - Lower Band)
pos_low = (Low - Lower Band) / (Upper Band - Lower Band)
pos_close = (Close - Lower Band) / (Upper Band - Lower Band)
If the upper band is not greater than the lower band or if the data is invalid (e.g., na), it defaults to 0.5 (the midline).
The average of these three positions (avg_pos) represents the normalized position for that timeframe, ranging from 0 (at the lower band) to 1 (at the upper band).
Multi-Timeframe Averaging:
The indicator fetches Bollinger Band data from four customizable timeframes (default: 30min, 60min, 240min, daily) using request.security() with lookahead=barmerge.lookahead_on to get the latest available data.
It calculates the normalized position (pos1, pos2, pos3, pos4) for each timeframe using f_getBBPosition().
These four positions are then averaged to produce the final avg_position:avg_position = (pos1 + pos2 + pos3 + pos4) / 4
This average is the oscillator value, which is plotted and typically oscillates between 0 and 1.
Moving Averages:
Two optional moving averages (MA1 and MA2) of the avg_position can be enabled, calculated using simple moving averages (ta.sma) with customizable lengths (default: 5 and 10).
These can be potentially used for MA crossover strategies.
What Is Being Averaged?
The oscillator (avg_position) is the average of the normalized price positions within the Bollinger Bands across the four selected timeframes. Specifically:It averages the avg_pos values (pos1, pos2, pos3, pos4) calculated for each timeframe.
Each avg_pos is itself an average of the normalized positions of the high, low, and close prices relative to the Bollinger Bands for that timeframe.
This multi-timeframe averaging smooths out short-term fluctuations and provides a broader perspective on the price's position within the volatility bands.
Interpretation
0.0 The price is at or below the lower Bollinger Band across all timeframes (indicating potential oversold conditions).
0.15: A customizable level (green band) which can be used for exiting short positions or entering long positions.
0.5: The midline, where the price is at the average of the Bollinger Bands (neutral zone).
0.85: A customizable level (orange band) which can be used for exiting long positions or entering short positions.
1.0: The price is at or above the upper Bollinger Band across all timeframes (indicating potential overbought conditions).
The colored regions and moving averages (if enabled) help identify trends or crossovers for trading signals.
Example
If the 30min timeframe shows the close at the upper band (position = 1.0), the 60min at the midline (position = 0.5), the 240min at the lower band (position = 0.0), and the daily at the upper band (position = 1.0), the avg_position would be:(1.0 + 0.5 + 0.0 + 1.0) / 4 = 0.625
This value (0.625) would plot in the orange region (between 0.85 and 0.5), suggesting the price is relatively strong but not at an extreme.
Notes
The use of lookahead=barmerge.lookahead_on ensures the indicator uses the latest available data, making it more real-time, though its effectiveness depends on the chart timeframe and TradingView's data feed.
The indicator’s sensitivity can be adjusted by changing bb_length ("Bollinger Band MA Length" in the Input tab), bb_mult ("Bollinger Band Standard Deviation," also in the Input tab), or the selected timeframes.
