Nebula Volatility and Compression Radar (TechnoBlooms)This dynamic indicator spots volatility compression and expansion zones, highlighting breakout opportunities with precision. Featuring vibrant Bollinger Bands, trend-colored candles and real-time signals, Nebula Volatility and Compression Radar (NVCR) is your radar for navigating price moves.
Key Features:-
1. Gradient Bollinger Bands - Visually stunning bands with gradient fills for clear price boundaries.
The gradient filling is coded simply so that even beginners can easily understand the concept. Trader can change the gradient color according to their preference.
fill(pupBB, pbaseBB,upBB,baseBB,top_color=color.rgb(238, 236, 94), bottom_color=color.new(chart.bg_color,100),title = "fill color", display =display.all,fillgaps = true,editable = false)
fill(pbaseBB, plowBB,baseBB,lowBB,top_color=color.new(chart.bg_color,100),bottom_color=color.rgb(230, 20, 30),title = "fill color", display =display.all,fillgaps = true,editable = false)
These two lines are used for giving gradient shades. You can change the colors as per your wish to give preferred color combination.
For Example:
Another Example:
2. Customizable Settings - Adjust Bollinger Bands, ATR and trend lengths to fit your trading styles.
3. Trend Insights - Candles turn green for uptrends, red for downtrends, and gray for neutral zones.
Nebula Volatility and Compression Radar create dynamic cloud like zones that illuminate trends with clarity.
Tìm kiếm tập lệnh với "bands"
ReadyFor401ks Just Tell Me When!ReadyFor401ks Just Tell Me When!
LET ME START BY SAYING. NO INDICATOR WILL HELP YOU NAIL THE PERFECT ENTRY/EXIT ON A TRADE. YOU SHOULD ALWAYS EDUCATE YOURSELF AND HAVE A BASIC UNDERSTANDING OF INVESTING, TRADING, CHART ANALYSIS, AND THE RISKS INVOLVED WITH. THAT BEING SAID, WITH THE RIGHT ADJUSTMENTS, IT'S PRETTY D*$N CLOSE TO PERFECTION!
This indicator is designed to help traders identify t rend direction, continuation signals, and potential exits based on a dynamic blend of moving averages, ATR bands, and price action filters. Whether you’re an intraday trader scalping the 5-minute chart or a swing trader analyzing the weekly timeframe for LEAPS , this tool provides a clear, rule-based system to help guide your trading decisions.
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Key Features & Benefits
🔹 Customizable Trend Power (Baseline) Calculation
• Choose from JMA, EMA, HMA, TEMA, DEMA, SMA, VAMA, and WMA for defining your baseline trend direction.
• The baseline helps confirm whether the market is in a bullish or bearish phase.
🔹 ATR-Based Trend Continuation & Volatility Measurement
• ATR bands dynamically adjust to market conditions, helping you spot breakouts and fakeouts.
• The indicator detects when price violates ATR range , which often signals impulse moves.
🔹 Clear Entry & Exit Signals
• Uses a Continuation MA (SSL2) to confirm trends.
• Includes a separate Exit MA (SSL3) that provides crossover signals to indicate when to exit trades or reverse positions .
• Plots trend continuation circles when ATR conditions align with trend signals.
🔹 Keltner Channel Baseline for Market Structure
• A modified Keltner Channel is integrated into the baseline to help filter out choppy conditions .
• If price remains inside the baseline, the market is in consolidation , while breakouts beyond the bands indicate strong trends .
🔹 Adaptive Color Coding for Market Conditions
• Bars change color based on momentum, making trend direction easy to read.
• Green = Bullish Trend, Red = Bearish Trend, Gray = Neutral/Chop.
🔹 Flexible Alerts for Trade Management
• Get real-time alerts when the Exit MA crosses price , helping you l ock in profits or switch directions .
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How to Use This Indicator for Different Trading Styles
🟢 For Intraday Trading (5-Minute Chart Setup)
• Faster MA settings help react quickly to momentum shifts.
• Ideal for scalping breakouts, trend continuation setups, and intraday reversals.
• Watch for ATR violations and price interacting with the baseline/Keltner Channel for entries.
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My Settings for Intraday Trading on 5min Chart
ATR Period: 15
ATR Multi: 1
ATR Smoothing: WMA
Trend Power based off of: JMA
Trend Power Period: 30
Continuation Type: JMA
Continuation Length: 20
Calculate Exit of what MA?: HMA
Calculate Exit off what Period? 30
Source of Exit Calculation: close
JMA Phase *APPLIES TO JMA ONLY: 3
JMA Power *APPLIES TO JMA ONLY: 3
Volatility Lookback Period *APPLIES TO VAMA ONLY 30
Use True Range for Channel? Checked
Base Channel Multiplier: 0.4
ATR Continuation Criteria: 1.1
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🔵 For Swing Trading & LEAPS (Weekly Chart Setup - Default Settings)
• Slower MAs provide a broader view of trend structure.
• Helps capture multi-week trend shifts and confirm entry points for longer-term trades.
• Weekly ATR bands highlight when stocks are entering overextended conditions.
💡 Example:
Let’s say you’re looking at TSLA on a Weekly Chart using the default settings. You notice that price crosses above the continuation MA (SSL2) while remaining above the baseline (trend power MA). The bar turns green, and price breaks above ATR resistance, signaling a strong bullish continuation. This could be a great opportunity to enter a long-term swing trade or LEAPS options position.
On the flip side, if price reverses below the Exit MA (SSL3) and turns red while breaking the lower ATR band, it might signal a good time to exit longs or enter a short trade.
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Final Thoughts
The ReadyFor401ks Just Tell Me When! indicator is an all-in-one trading system that simplifies trend-following, volatility measurement, and trade management. By integrating multiple moving average types, ATR filters, and clear visual cues, it allows traders to stay disciplined and remove emotions from their trading decisions.
✅ Perfect for scalpers, day traders, and swing traders alike!
🔔 Set up alerts for automated trade signals and never miss a key move!
💬 If you find this indicator useful, leave a comment and share how you use it in your trading! 🚀
Red & Green Zone ReversalOverview
The “Red & Green Zone Reversal” indicator is designed to visually highlight potential reversal zones on your chart by using a combination of Bollinger Bands and the Relative Strength Index (RSI).
It overlays on the chart and provides background color cues—red for oversold conditions and green for overbought conditions—along with corresponding alert triggers.
Key Components
Overlay: The indicator is set to overlay the chart, meaning its visual cues (colored backgrounds) are drawn directly on the price chart.
Bollinger Bands Calculation
Period: A 20-period simple moving average (SMA) is calculated from the closing prices.
Standard Deviation Multiplier: A multiplier of 2.0 is applied.
Bands Defined:
Basis: The 20-period SMA.
Deviation: Calculated as 2 times the standard deviation over the same period.
Upper Band: Basis plus the deviation.
Lower Band: Basis minus the deviation.
RSI Calculation
Period: The RSI is computed over a 14-period span using the closing prices.
Thresholds:
Oversold Threshold: 30 (used for the red zone condition).
Overbought Threshold: 70 (used for the green zone condition).
Zone Conditions
Red Zone (Oversold):
Criteria: The price is below the lower Bollinger Band and the RSI is below 30.
Purpose: Highlights a situation where the asset may be deeply oversold, signaling a potential reversal to the upside.
Green Zone (Overbought):
Criteria: The price is above the upper Bollinger Band and the RSI is above 70.
Purpose: Indicates that the asset may be overbought, potentially signaling a reversal to the downside.
Visual and Alert Components
Background Coloring:
Red Background: Applied when the red zone condition is met (using a semi-transparent red).
Green Background: Applied when the green zone condition is met (using a semi-transparent green).
Alerts:
Red Alert: An alert condition titled “Deep Oversold Alert” is triggered with the message “Deep Oversold Signal triggered!” when the red zone criteria are satisfied.
Green Alert: Similarly, an alert condition titled “Deep Overbought Alert” is triggered with the message “Deep Overbought Signal triggered!” when the green zone criteria are met.
Important Disclaimers
Not Financial Advice:
This indicator is provided for informational and analytical purposes only. It does not constitute trading advice or a recommendation to buy or sell any asset. Traders should use it as one of several tools in their analysis and should perform their own due diligence.
Risk Management:
Trading inherently involves risk. Past performance is not indicative of future results. Always implement appropriate risk management and use stop losses where necessary.
Summary
In summary, the “Red & Green Zone Reversal” indicator uses Bollinger Bands and RSI to detect extreme market conditions. It visually marks oversold (red) and overbought (green) conditions directly on the chart and offers alert conditions to help traders monitor these potential reversal points.
Enjoy!!
BK BB Horizontal LinesIndicator Description:
I am incredibly proud and excited to share my second indicator with the TradingView community! This tool has been instrumental in helping me optimize my positioning and maximize my trades.
Bollinger Bands are a critical component of my trading strategy. I designed this indicator to work seamlessly alongside my previously introduced tool, "BK MA Horizontal Lines." This indicator focuses specifically on the Daily Bollinger Bands, applying horizontal lines to the bands which is displayed in all timeframes. The Daily bands in my opinion hold a strong significance when it comes to support and resistance, knowing your current positioning and maximizing your trades. The settings are fully adjustable to suit your preferences and trading style.
If you find success with this indicator, I kindly ask that you give back in some way through acts of philanthropy, helping others in the best way you see fit.
Good luck to everyone, and always remember: God gives us everything. May all the glory go to the Almighty!
Sector ETFs performance overviewThe indicator provides a nuanced view of sector performance through ETF analysis, focusing on long-term price trends and deviations from these trends to gauge relative strength or weakness. It utilizes a methodical approach to smooth out ETF price data and then applies a regression analysis to pinpoint the primary trend direction. By examining how far the current price deviates from this regression line, the indicator identifies potential overbought or oversold conditions within various sectors.
Core Analysis Techniques:
Logarithmic Transformation and Regression: This process transforms ETF closing prices on a logarithmic scale to better understand sector growth patterns and dynamics. A linear regression of these prices helps define the overarching trend, crucial for understanding market movements.
Volatility Bands for Market State Assessment: The indicator calculates standard deviation based on logarithmic prices to establish dynamic bands around the regression line. These bands are instrumental in identifying market states, highlighting when sectors may be overextended from their central trend.
Sector-Specific Analysis: By focusing on distinct sector ETFs, the tool enables targeted analysis across various market segments. This specificity allows for a granular look at sectors like technology, healthcare, and financials, providing insights tailored to each area.
Adaptability and Insight:
Customizable Parameters: The indicator offers users the ability to adjust key parameters such as regression length and smoothing factors. This customization ensures that the analysis can be tailored to individual preferences and market outlooks.
Trend Direction and Momentum: It assesses the ETF's price movement relative to historical data and the established volatility bands, helping to clarify the sector's trend strength and potential directional shifts.
Strategic Application:
Focusing on trend and volatility analysis rather than direct trading signals, the indicator aids in forming a strategic view of sector investments. It's particularly useful for:
Spotting macroeconomic trends through the lens of sector ETF performance.
Informing portfolio decisions with nuanced insights into sector momentum and market conditions.
Anticipating potential market shifts by evaluating how current prices align with historical volatility and trend patterns.
This tool stands out as a vital resource for analyzing sector-level market trends, offering detailed insights into the dynamics of economic sectors for comprehensive market analysis.
Nifty 50 5mint Strategy
The script defines a specific trading session based on user inputs. This session is specified by a time range (e.g., "1000-1510") and selected days of the week (e.g., Monday to Friday). This session definition is crucial for trading only during specific times.
Lookback and Breakout Conditions:
The script uses a lookback period and the highest high and lowest low values to determine potential breakout points. The lookback period is user-defined (default is 10 periods).
The script also uses Bollinger Bands (BB) to identify potential breakout conditions. Users can enable or disable BB crossover conditions. BB consists of an upper and lower band, with the basis.
Additionally, the script uses Dema (Double Exponential Moving Average) and VWAP (Volume Weighted Average Price) . Users can enable or disable this condition.
Buy and Sell Conditions:
Buy conditions are met when the close price exceeds the highest high within the specified lookback period, Bollinger Bands conditions are satisfied, Dema-VWAP conditions are met, and the script is within the defined trading session.
Sell conditions are met when the close price falls below the lowest low within the lookback period, Bollinger Bands conditions are satisfied, Dema-VWAP conditions are met, and the script is within the defined trading session.
When either condition is met, it triggers a "long" or "short" position entry.
Trailing Stop Loss (TSL):
Users can choose between fixed points ( SL by points ) or trailing stop (Profit Trail).
For fixed points, users specify the number of points for the stop loss. A fixed stop loss is set at a certain distance from the entry price if a position is opened.
For Profit Trail, users can enable or disable this feature. If enabled, the script uses a "trail factor" (lookback period) to determine when to adjust the stop loss.
If the price moves in the direction of the trade and reaches a certain level (determined by the trail factor), the stop loss is adjusted, trailing behind the price to lock in profits.
If the close price falls below a certain level (lowest low within the trail factor(lookback)), and a position is open, the "long" position is closed (strategy.close("long")).
If the close price exceeds a certain level (highest high within the specified trail factor(lookback)), and a position is open, the "short" position is closed (strategy.close("short")).
Positions are also closed if they are open outside of the defined trading session.
Background Color:
The script changes the background color of the chart to indicate buy (green) and sell (red) signals, making it visually clear when the strategy conditions are met.
In summary, this script implements a breakout trading strategy with various customizable conditions, including Bollinger Bands, Dema-VWAP crossovers, and session-specific rules. It also includes options for setting stop losses and trailing stop losses to manage risk and lock in profits. The "trail factor" helps adjust trailing stops dynamically based on recent price movements. Positions are closed under certain conditions to manage risk and ensure compliance with the defined trading session.
CE=Buy, CE_SL=stoploss_buy, tCsl=Trailing Stop_buy.
PE=sell, PE_SL= stoploss_sell, tpsl=Trailing Stop_sell.
Remember that trading involves inherent risks, and past performance is not indicative of future results. Exercise caution, manage risk diligently, and consider the advice of financial experts when using this script or any trading strategy.
God's Little FingerThe "God's Little Finger" indicator uses several technical analysis tools to provide information about the direction of the market and generate buy/sell signals. These tools include a 200-period exponential moving average (EMA), Moving Average Convergence Divergence (MACD), Bollinger Bands, and the Relative Strength Index (RSI).
EMA is used to determine if prices are trending. MACD measures the speed and momentum of the trend. Bollinger Bands are used to determine if prices are staying within a range and to measure the strength of the trend. RSI shows overbought/oversold levels and can be used to determine if the trend will continue.
The indicator generates buy/sell signals based on market conditions. A buy signal is generated when the MACD line is below zero, the price is below the lower boundary of the Bollinger Bands, the price is above the 200-period EMA, and the RSI is in oversold levels (usually below 40). A sell signal is generated when the MACD line is above zero, the price is above the upper boundary of the Bollinger Bands, the price is below the 200-period EMA, and the RSI is in overbought levels (usually above 60).
However, it should be noted that indicators can be used to predict market conditions, but they do not guarantee results and any changes or unexpected events in the market can affect predictions. Therefore, they should always be used in conjunction with other analysis methods and risk management strategies.
Waddah Attar Explosion with TDI First of all, a big shoutout to @shayankm, @LazyBear, @Bromley, @Goldminds and @LuxAlgo, the ones that made this script possible.
This is a version of Waddah Attar Explosion with Traders Dynamic Index.
WAE provides volume and volatility information. Also, WAE calculation was changed to a full-on MACD, to provide the momentum: the idea is to "assess" which MACD bars have significant momentum (i.e. crossover the Explosion Line)
TDI provides momentum, divergences as well as overbought and oversold areas. There is also a RSI on a different timeframe, for convergence.
Almost everything is editable:
- All moving averages are customizable, including the TRAMA, from @LuxAlgo
Waddah Attar Explosion_
- Three different crossing signals: histogram crossing contracting Explosion Line, expanding Explosion Line and ascending Explosion Line while both Bolling Bands are expanding; Explosion Line shows different color when expanding.
- Explosion line signals: Below DeadZone line and Exhaustion (highest value in a given lookback period). You can set a predefined EPL slope to filter out some noise.
- Deadzone signal : Deadzone squeeze ( lowst value in a given lookback period)
TDI:
- Overbought an Oversold signals. The OB and OS shapes have two colors, in order to display extreme signals on current timeframe or extreme signals on current and different time frame.
- Visual display of RSI outside the Bollinger Bands, and crossing of RSI Moving Average crossing of zero line.
I believe this combination is great for so many reasons!
Like the idea of TTM Squeeze? You can tune the Deadzone and Explosion lines to look for a volatility breakout
Like trading divergences or want to filter out extreme areas? The RSI is great for that
You like the using the MACD strategy but don't like the amount of false signals given? this WAE version filters some of them out.
If you are a Bollinger bands fan, you can customize both indicators to trade breakouts and/or mean reversion strategies, and filter out exhaustion of the bands expansion
This is my first publication, so give it a go and provide feedback if possible.
BB-EMA-MAWikipedia: Bollinger Bands are a type of statistical chart characterizing the prices and volatility over time of a financial instrument or commodity, using a formulaic method propounded by John Bollinger in the 1980s. Financial traders employ these charts as a methodical tool to inform trading decisions, control automated trading systems, or as a component of technical analysis. Bollinger Bands display a graphical band (the envelope maximum and minimum of moving averages, similar to Keltner or Donchian channels) and volatility (expressed by the width of the envelope) in one two-dimensional chart.
If you set Type = 2 then it will use EMA average for Bollinger bands .
If you set Type = 1 then it will use MA average for Bollinger bands .
Default settings is moving average with period 50
When price move to standard Deviation (std) +2 and std +3 is signal for sell ( selling zone)
When price move to standard Deviation (std) -2 and std -3 is signal for sell ( buying zone)
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated
Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA.
This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com
Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types.
STD-Stepped, Variety N-Tuple Moving Averages
STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the calculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code:
temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1))
array.set(workarr, k + 1, temp)
After we pack the array, we apply the coefficients to derive the NTMA:
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Bands/Channels
See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier
Signals
Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short.
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
6 bands/channels types
6 stepping types
Related indicators
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping
STD-Stepped Fast Cosine Transform Moving Average
ATR-Stepped PDF MA
[blackcat] L1 Vitali Apirine MABLevel 1
Background
Vitali Apirine’s articles in the July & August issues on 2021, “Moving Average Bands”
Function
In “Moving Average Bands” (part 1, July 2021 issue) and “Moving Average Band Width” (part 2, August 2021 issue), author Vitali Apirine explains how moving average bands (MAB) can be used as a trend-following indicator by displaying the movement of a shorter-term moving average in relation to the movement of a longer-term moving average. The distance between the bands will widen as volatility increases and will narrow as volatility decreases.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
Volatility ChannelThis script is based on an idea I have had for bands that react better to crypto volatility. It calculates a Donchian Channel, SMMA-Smoothed True Range, Bollinger Bands (standard deviation), and a Keltner Channel (average true range) and averages the components to construct its bands/envelopes. This way, hopefully band touches are a more reliable indicator of a temporary bottom, and so on. Secondary coloring for strength of trend is given as a gradient based on RSI.
DrFX Reversal Algo - MACD-RSI System with Dynamic Zone Filtering**DrFX Reversal Algo** is a sophisticated reversal detection system that combines MACD momentum analysis with RSI confirmation and dynamic support/resistance zone filtering. This indicator employs advanced mathematical filtering techniques to identify high-probability reversal points while minimizing false signals through intelligent zone-based filtering.
**Core Innovation & Originality**
This system uniquely integrates four key analytical components:
1. **Enhanced MACD Engine** - Customizable fast (20), slow (50), and signal (12) lengths with crossover/crossunder detection optimized for reversal identification
2. **RSI Power Classification** - 14-period RSI used to classify signal strength and trend bias, distinguishing between "Strong" and regular signals
3. **Kalman-Filtered Dynamic Zones** - Advanced mathematical smoothing of support/resistance levels using Kalman filter algorithms for noise reduction
4. **Gradient-Based Visual System** - Power-weighted bar coloring that visualizes trend strength using MACD histogram and RSI signal intensity
**System Architecture & Functionality**
**Signal Generation Methodology:**
The core algorithm detects MACD line crossovers above and below the signal line, then applies RSI-based classification. When RSI signal (RSI-50) is positive during bullish MACD crossovers, the system generates "Strong Buy" signals. When RSI signal is negative or neutral, it produces regular "Buy" signals. The inverse logic applies for sell signals.
**Dynamic Zone Calculation:**
Support and resistance zones are calculated using a multi-step process:
1. **Volatility Bands**: ATR-based upper/lower bands using (high+low)/2 ± ATR * multiplier
2. **Precise Zone Definition**: Integration of 20-period highest/lowest calculations with volatility bands
3. **Kalman Filter Smoothing**: Advanced noise reduction using configurable Q (0.01) and R (0.1) parameters
4. **Zone Validation**: Real-time adjustment based on price action and volatility changes
**Kalman Filter Implementation:**
The system employs a custom Kalman filter function for zone smoothing:
```
kf_k = kf_p / (kf_p + kf_r)
kf_x = kf_k * input + (1 - kf_k) * previous_estimate
kf_p = (1 - kf_k) * kf_p + kf_q
```
This mathematical approach reduces zone boundary noise while maintaining responsiveness to genuine support/resistance level changes.
**Unique Visual Features**
**Power-Based Gradient System:**
Bar coloring utilizes a sophisticated gradient calculation based on MACD histogram power (absolute value) and RSI signal strength. The system creates dynamic color transitions:
- **Bullish Gradient**: Green spectrum (0-255 intensity) based on histogram power
- **Bearish Gradient**: Red spectrum (0-255 intensity) based on histogram power
- **Consolidation**: Mixed gradient indicating uncertain market conditions
**Dynamic Zone Visualization:**
- **Support Zones**: Blue-filled areas between smoothed support boundaries
- **Resistance Zones**: Red-filled areas between smoothed resistance boundaries
- **Zone Adaptation**: Real-time boundary adjustment based on volatility and price action
**Signal Classification System**
**Signal Strength Hierarchy:**
1. **Strong Buy**: MACD bullish crossover + RSI signal > 0 (above 50-line)
2. **Regular Buy**: MACD bullish crossover + RSI signal ≤ 0 (below 50-line)
3. **Strong Sell**: MACD bearish crossover + RSI signal < 0 (below 50-line)
4. **Regular Sell**: MACD bearish crossover + RSI signal ≥ 0 (above 50-line)
**Optional Zone Filtering:**
When enabled, the system only displays signals when:
- Buy signals: Price above smoothed support zone end
- Sell signals: Price below smoothed resistance zone start
**Usage Instructions**
**Primary Signal Interpretation:**
- **Large Green Triangles**: Strong buy signals with RSI confirmation above 50
- **Small Green Triangles**: Regular buy signals with RSI below 50
- **Large Red Triangles**: Strong sell signals with RSI confirmation below 50
- **Small Red Triangles**: Regular sell signals with RSI above 50
**Zone Analysis:**
- **Blue Zones**: Dynamic support areas where buying interest may emerge
- **Red Zones**: Dynamic resistance areas where selling pressure may increase
- **Zone Breaks**: Price movement outside zones indicates potential trend continuation
**Bar Color Interpretation:**
- **Bright Green**: Strong bullish momentum (high MACD histogram power + positive RSI)
- **Dark Green**: Moderate bullish momentum
- **Bright Red**: Strong bearish momentum (high MACD histogram power + negative RSI)
- **Dark Red**: Moderate bearish momentum
- **Mixed Colors**: Consolidation or uncertain trend direction
**Optimal Usage Strategies:**
1. **Reversal Trading**: Focus on signals occurring near zone boundaries
2. **Confirmation Trading**: Use zone filter to reduce false signals in trending markets
3. **Momentum Trading**: Prioritize "Strong" signals with bright gradient bar coloring
4. **Multi-Timeframe**: Combine with higher timeframe trend analysis for context
**Parameter Customization**
**MACD Settings:**
- **Fast Length (20)**: Shorter periods increase sensitivity
- **Slow Length (50)**: Longer periods reduce noise
- **Signal Smoothing (12)**: Affects crossover signal timing
**Support/Resistance Settings:**
- **Volatility Period (10)**: ATR calculation period for zone width
- **Multiplier (5.0)**: Zone expansion factor based on volatility
**Visual Settings:**
- **Gradient Range (2000)**: Controls color intensity scaling
- **Zone Filtering**: Enables/disables signal filtering based on zone position
**Advanced Features**
**Alert System:**
Comprehensive alert functionality with detailed messages including symbol, timeframe, current price, and signal type. Separate enable/disable options for long and short alerts.
**Mathematical Precision:**
The Kalman filter implementation provides superior noise reduction compared to simple moving averages while maintaining responsiveness to genuine market structure changes.
**Important Considerations**
This system works optimally in markets with clear support/resistance levels and moderate volatility. The Kalman filter smoothing may introduce slight lag during rapid market movements. Strong signals generally provide higher probability setups but may be less frequent than regular signals.
The algorithm combines established techniques (MACD, RSI) with advanced filtering and zone detection methodologies. The integration of multiple confirmation methods helps reduce false signals while maintaining sensitivity to genuine reversal opportunities.
**Disclaimer**: This indicator is designed for educational and analytical purposes. Past performance does not guarantee future results. The system's effectiveness varies across different market conditions and timeframes. Always implement proper risk management and consider multiple confirmation methods before making trading decisions.
Playbook//@version=6
indicator('Playbook', overlay = true, scale = scale.right)
// === Inputs ===
useYesterdayPOC = input.bool(true, 'Use Yesterday\'s POC (else Today’s Developing)')
atrLength = input.int(14, 'ATR Length', minval = 1)
stretchMult = input.float(1.5, 'Stretch Threshold (in ATRs)', minval = 0.1, step = 0.1)
showBands = input.bool(true, "Show Stretch Bands")
useAnchoredVWAP = input.bool(true, "Show Anchored VWAP")
anchorDate = input.time(timestamp("01 Jan 2023 00:00 +0000"), "VWAP Anchor Date")
// === ATR ===
atr = ta.atr(atrLength)
isNewDay = ta.change(time('D')) != 0
// === VWAP as POC Approximation ===
todayVWAP = ta.vwap
var float yVWAP = na
if isNewDay
yVWAP := todayVWAP
activePOC = useYesterdayPOC and not na(yVWAP) ? yVWAP : todayVWAP
// === Stretch Bands ===
upperBand = activePOC + atr * stretchMult
lowerBand = activePOC - atr * stretchMult
// Plot stretch bands
pocColor = color.yellow
bandFill = plot(upperBand, "Upper Band", color=color.red, linewidth=1, display=showBands ? display.all : display.none)
bandFill2 = plot(lowerBand, "Lower Band", color=color.green, linewidth=1, display=showBands ? display.all : display.none)
pocLine = plot(activePOC, "POC Target", color=pocColor, linewidth=2)
fill(bandFill, bandFill2, color=color.new(color.gray, 90))
// === Anchored VWAP ===
anchoredVWAP = ta.vwap(ta.change(time) >= anchorDate ? close : na)
plot(useAnchoredVWAP ? anchoredVWAP : na, "Anchored VWAP", color=color.blue, linewidth=2)
// === STATUS TABLE ===
var table statusTable = table.new(position.bottom_right, 1, 1, border_width=1, border_color=color.gray)
insideBands = close <= upperBand and close >= lowerBand
statusText = insideBands ? "WAIT" : "TRADE AVAILABLE"
statusColor = insideBands ? color.orange : color.green
table.cell(statusTable, 0, 0, statusText, text_color=color.rgb(5, 4, 4), bgcolor=statusColor)
// === Heatmap ===
bgcolor(close > upperBand ? color.new(color.red, 80) : close < lowerBand ? color.new(color.green, 80) : color.new(color.orange, 90))
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
╰──────────────────────────────────────────────╯
🔍 Overview
The Squeeze Momentum Regression Clouds (SMRC) indicator is a powerful visual tool for identifying price compression , trend strength , and slope momentum using multiple layers of linear regression Clouds. Designed to extend the classic squeeze framework, this indicator captures the behavior of price through dynamic slope detection, percentile-based spread analytics, and an optional UI for trend inspection — across up to four customizable regression Clouds .
────────────────────────────────────────────────────────────
╭────────────────╮
⚙️ Core Features
╰────────────────╯
Up to 4 Regression Clouds – Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine – Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap – Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring – Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles – Optional candle coloring reflects the real-time composite score.
UI Table – A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling – Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options – Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
────────────────────────────────────────────────────────────
╭──────────────────────────────────────────╮
🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
╰──────────────────────────────────────────╯
Each Cloud changes color based on its current slope:
Green indicates a rising trend.
Red indicates a falling trend.
Gray indicates a flat slope — often seen during chop or transitions.
Cloud 1 typically reflects short-term structure, while Cloud 4 represents long-term directional bias. Watch for multi-Cloud alignment — when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
────────────────────────────────────────────────────────────
╭───────────────────────────────────────────────╮
2. Use Compression Heat to Anticipate Breakouts
╰───────────────────────────────────────────────╯
The space between each Cloud’s top and bottom regression lines is measured, normalized, and analyzed over time. When this spread tightens relative to its history, the script highlights the band with a yellow compression glow .
This visual cue helps identify squeeze zones before volatility expands. If you see compression paired with a changing slope color (e.g., gray to green), this may indicate an impending breakout.
────────────────────────────────────────────────────────────
╭─────────────────────────────────╮
3. Leverage the Optional Table UI
╰─────────────────────────────────╯
The indicator includes a dynamic, floating table that displays real-time metrics per Cloud. These include:
Slope direction and value , with historical Min/Max reference.
Top and Bottom percentile ranks , showing how price sits within the Cloud range.
Current spread width , compared to its historical norms.
Composite score , which blends trend, slope, and compression for that Cloud.
You can customize the table’s position, theme, transparency, and whether to show a combined summary score in the header.
────────────────────────────────────────────────────────────
╭─────────────────────────────────────────────╮
4. Analyze Candle Color for Composite Signals
╰─────────────────────────────────────────────╯
When enabled, the indicator colors candles based on a weighted composite score. This score factors in:
The signed slope of each Cloud (up, down, or flat)
The percentile pressure from the top and bottom bands
The degree of spread compression
Expect green candles in bullish trend phases, red candles during bearish regimes, and gray candles in mixed or low-conviction zones.
Candle coloring provides a visual shorthand for market conditions , useful for intraday scanning or historical backtesting.
────────────────────────────────────────────────────────────
╭────────────────────────╮
🧰 Configuration Guidance
╰────────────────────────╯
To tailor the indicator to your strategy:
Use Cloud lengths like 21, 34, 55, and 89 for a balanced multi-timeframe view.
Adjust the slope threshold (default 0.05) to control how sensitive the trend coloring is.
Set the spread floor (e.g., 0.15) to tune when compression is detected and visualized.
Choose your weighting style : Inverse Length (favor faster bands), Equal, or Max Pooling (most aggressive).
Set composite weights to emphasize trend slope, percentile bias, or compression—depending on your market edge.
────────────────────────────────────────────────────────────
╭────────────────╮
✅ Best Practices
╰────────────────╯
Use aligned Cloud colors across all bands to confirm trend conviction.
Combine slope direction with compression glow for early breakout entry setups.
In choppy markets, watch for Clouds 1 and 2 turning flat while Clouds 3 and 4 remain directional — a sign of potential trend exhaustion or consolidation.
Keep the table enabled during backtesting to manually evaluate how each Cloud behaved during price turns and consolidations.
────────────────────────────────────────────────────────────
╭───────────────────────╮
📌 License & Usage Terms
╰───────────────────────╯
This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
✅ You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
❌ You are not allowed to:
Resell or redistribute the script without permission
Use it inside any paid product or service
Republish without giving clear attribution to the original author
For commercial licensing , private customization, or collaborations, please contact Joshua Danford directly.
Volatility-Adjusted Momentum Score (VAMS) [QuantAlgo]🟢 Overview
The Volatility-Adjusted Momentum Score (VAMS) measures price momentum relative to current volatility conditions, creating a normalized indicator that identifies significant directional moves while filtering out market noise. It divides annualized momentum by annualized volatility to produce scores that remain comparable across different market environments and asset classes.
The indicator displays a smoothed VAMS Z-Score line with adaptive standard deviation bands and an information table showing real-time metrics. This dual-purpose design enables traders and investors to identify strong trend continuation signals when momentum persistently exceeds normal levels, while also spotting potential mean reversion opportunities when readings reach statistical extremes.
🟢 How It Works
The indicator calculates annualized momentum using a simple moving average of logarithmic returns over a specified period, then measures annualized volatility through the standard deviation of those same returns over a longer timeframe. The raw VAMS score divides momentum by volatility, creating a risk-adjusted measure where high volatility reduces scores and low volatility amplifies them.
This raw VAMS value undergoes Z-Score normalization using rolling statistical parameters, converting absolute readings into standardized deviations that show how current conditions compare to recent history. The normalized Z-Score receives exponential moving average smoothing to create the final VAMS line, reducing false signals while preserving sensitivity to meaningful momentum changes.
The visualization includes dynamically calculated standard deviation bands that adjust to recent VAMS behavior, creating statistical reference zones. The information table provides real-time numerical values for VAMS Z-Score, underlying momentum percentages, and current volatility readings with trend indicators.
🟢 How to Use
1. VAMS Z-Score Bands and Signal Interpretation
Above Mean Line: Momentum exceeds historical averages adjusted for volatility, indicating bullish conditions suitable for trend following
Below Mean Line: Momentum falls below statistical norms, suggesting bearish conditions or downward pressure
Mean Line Crossovers: Primary transition signals between bullish and bearish momentum regimes
1 Standard Deviation Breaks: Strong momentum conditions indicating statistically significant directional moves worth following
2 Standard Deviation Extremes: Rare momentum readings that often signal either powerful breakouts or exhaustion points
2. Information Table and Market Context
Z-Score Values: Current VAMS reading displayed in standard deviations (σ), showing how far momentum deviates from its statistical norm
Momentum Percentage: Underlying annualized momentum displayed as percentage return, quantifying the directional strength
Volatility Context: Current annualized volatility levels help interpret whether VAMS readings occur in high or low volatility environments
Trend Indicators: Directional arrows and change values provide immediate feedback on momentum shifts and market transitions
3. Strategy Applications and Alert System
Trend Following: Use sustained readings beyond the mean line and 1σ band penetrations for directional trades, especially when VAMS maintains position in upper or lower statistical zones
Mean Reversion: Focus on 2σ extreme readings for contrarian opportunities, particularly effective in sideways markets where momentum tends to revert to statistical norms
Alert Notifications: Built-in alerts for mean crossovers (regime changes), 1σ breaks (strong signals), and 2σ touches (extreme conditions) help monitor multiple instruments for both continuation and reversal setups
Quantum Reversal# 🧠 Quantum Reversal
## **Quantitative Mean Reversion Framework**
This algorithmic trading system employs **statistical mean reversion theory** combined with **adaptive volatility modeling** to capitalize on Bitcoin's inherent price oscillations around its statistical mean. The strategy integrates multiple technical indicators through a **multi-layered signal processing architecture**.
---
## ⚡ **Core Technical Architecture**
### 📊 **Statistical Foundation**
- **Bollinger Band Mean Reversion Model**: Utilizes 20-period moving average with 2.2 standard deviation bands for volatility-adjusted entry signals
- **Adaptive Volatility Threshold**: Dynamic standard deviation multiplier accounts for Bitcoin's heteroscedastic volatility patterns
- **Price Action Confluence**: Entry triggered when price breaches lower volatility band, indicating statistical oversold conditions
### 🔬 **Momentum Analysis Layer**
- **RSI Oscillator Integration**: 14-period Relative Strength Index with modified oversold threshold at 45
- **Signal Smoothing Algorithm**: 5-period simple moving average applied to RSI reduces noise and false signals
- **Momentum Divergence Detection**: Captures mean reversion opportunities when momentum indicators show oversold readings
### ⚙️ **Entry Logic Architecture**
```
Entry Condition = (Price ≤ Lower_BB) OR (Smoothed_RSI < 45)
```
- **Dual-Condition Framework**: Either statistical price deviation OR momentum oversold condition triggers entry
- **Boolean Logic Gate**: OR-based entry system increases signal frequency while maintaining statistical validity
- **Position Sizing**: Fixed 10% equity allocation per trade for consistent risk exposure
### 🎯 **Exit Strategy Optimization**
- **Profit-Lock Mechanism**: Positions only closed when showing positive unrealized P&L
- **Trend Continuation Logic**: Allows winning trades to run until momentum exhaustion
- **Dynamic Exit Timing**: No fixed profit targets - exits based on profitability state rather than arbitrary levels
---
## 📈 **Statistical Properties**
### **Risk Management Framework**
- **Long-Only Exposure**: Eliminates short-squeeze risk inherent in cryptocurrency markets
- **Mean Reversion Bias**: Exploits Bitcoin's tendency to revert to statistical mean after extreme moves
- **Position Management**: Single position limit prevents over-leveraging
### **Signal Processing Characteristics**
- **Noise Reduction**: SMA smoothing on RSI eliminates high-frequency oscillations
- **Volatility Adaptation**: Bollinger Bands automatically adjust to changing market volatility
- **Multi-Timeframe Coherence**: Indicators operate on consistent timeframe for signal alignment
---
## 🔧 **Parameter Configuration**
| Technical Parameter | Value | Statistical Significance |
|-------------------|-------|-------------------------|
| Bollinger Period | 20 | Standard statistical lookback for volatility calculation |
| Std Dev Multiplier | 2.2 | Optimized for Bitcoin's volatility distribution (95.4% confidence interval) |
| RSI Period | 14 | Traditional momentum oscillator period |
| RSI Threshold | 45 | Modified oversold level accounting for Bitcoin's momentum characteristics |
| Smoothing Period | 5 | Noise reduction filter for momentum signals |
---
## 📊 **Algorithmic Advantages**
✅ **Statistical Edge**: Exploits documented mean reversion tendency in Bitcoin markets
✅ **Volatility Adaptation**: Dynamic bands adjust to changing market conditions
✅ **Signal Confluence**: Multiple indicator confirmation reduces false positives
✅ **Momentum Integration**: RSI smoothing improves signal quality and timing
✅ **Risk-Controlled Exposure**: Systematic position sizing and long-only bias
---
## 🔬 **Mathematical Foundation**
The strategy leverages **Bollinger Band theory** (developed by John Bollinger) which assumes that prices tend to revert to the mean after extreme deviations. The RSI component adds **momentum confirmation** to the statistical price deviation signal.
**Statistical Basis:**
- Mean reversion follows the principle that extreme price deviations from the moving average are temporary
- The 2.2 standard deviation multiplier captures approximately 97.2% of price movements under normal distribution
- RSI momentum smoothing reduces noise inherent in oscillator calculations
---
## ⚠️ **Risk Considerations**
This algorithm is designed for traders with understanding of **quantitative finance principles** and **cryptocurrency market dynamics**. The strategy assumes mean-reverting behavior which may not persist during trending market phases. Proper risk management and position sizing are essential.
---
## 🎯 **Implementation Notes**
- **Market Regime Awareness**: Most effective in ranging/consolidating markets
- **Volatility Sensitivity**: Performance may vary during extreme volatility events
- **Backtesting Recommended**: Historical performance analysis advised before live implementation
- **Capital Allocation**: 10% per trade sizing assumes diversified portfolio approach
---
**Engineered for quantitative traders seeking systematic mean reversion exposure in Bitcoin markets through statistically-grounded technical analysis.**
Dynamic Flow Ribbons [BigBeluga]🔵 OVERVIEW
A dynamic multi-band trend visualization system that adapts to market volatility and reveals trend momentum with layered ribbon channels.
Dynamic Flow Ribbons transforms price action into flowing trend bands that expand and contract with volatility. It not only shows the active directional bias but also visualizes how strong or weak the trend is through layered ribbons, making it easier to assess trend quality and structure.
🔵 CONCEPTS
Uses an adaptive trend detection system built on a volatility envelope derived from an EMA of the average price (HLC3).
Measures volatility using a long-period average of the high-low range, which scales the envelope width dynamically.
Trend direction flips when the average price crosses above or below these envelopes.
Ribbons form around the trend line to show how far price is stretching or compressing relative to the mean.
🔵 FEATURES
Volatility-Based Trend Line:
A thick, color-coded line tracks the current trend with smoother transitions between phases.
Multi-Layered Flow Ribbons:
Up to 10 bands (5 above and 5 below) radiate outward from the upper and lower envelopes, reflecting volatility strength and direction.
Trend Coloring & Transitions:
Ribbons and candles are dynamically colored based on trend direction— green for bullish , orange for bearish . Transparency fades with distance from the core trend band.
Real-Time Responsiveness:
Ribbon structure and trend shifts update in real time, adapting instantly to fast market changes.
🔵 HOW TO USE
Use the color and thickness of the core trend line to follow directional bias.
When ribbons widen symmetrically, it signals strong trend momentum .
Narrowing or overlapping ribbons can suggest consolidation or transition zones .
Combine with breakout systems or volume tools to confirm impulsive or corrective phases .
Adjust the “Length” (factor) input to tune sensitivity—higher values smooth trends more.
🔵 CONCLUSION
Dynamic Flow Ribbons offers a sleek and insightful view into trend strength and structure. By visualizing volatility expansion with directional flow, it becomes a powerful overlay for momentum traders, swing strategists, and trend followers who want to stay ahead of evolving market flows
Trend Impulse Channels (Zeiierman)█ Overview
Trend Impulse Channels (Zeiierman) is a precision-engineered trend-following system that visualizes discrete trend progression using volatility-scaled step logic. It replaces traditional slope-based tracking with clearly defined “trend steps,” capturing directional momentum only when price action decisively confirms a shift through an ATR-based trigger.
This tool is ideal for traders who prefer structured, stair-step progression over fluid curves, and value the clarity of momentum-based bands that reveal breakout conviction, pullback retests, and consolidation zones. The channel width adapts automatically to market volatility, while the step logic filters out noise and false flips.
⚪ The Structural Assumption
This indicator is built on a core market structure observation:
After each strong trend impulse, the market typically enters a “cooling-off” phase as profit-taking occurs and counter-trend participants enter. This often results in a shallow pullback or stall, creating a slight negative slope in an uptrend (or a positive slope in a downtrend).
These “cooling-off” phases don’t reverse the trend — they signal temporary pressure before the next leg continues. By tracking trend steps discretely and filtering for this behavior, Trend Impulse Channels helps traders align with the rhythm of impulse → pause → impulse.
█ How It Works
⚪ Step-Based Trend Engine
At the heart of this tool is a dynamic step engine that progresses only when price crosses a predefined ATR-scaled trigger level:
Trigger Threshold (× ATR) – Defines how far price must break beyond the current trend state to register a new trend step.
Step Size (Volatility-Guided) – Each trend continuation moves the trend line in discrete units, scaling with ATR and trend persistence.
Trend Direction State – Maintains a +1/-1 internal bias to support directional filters and step tracking.
⚪ Volatility-Adaptive Channel
Each step is wrapped inside a dynamic envelope scaled to current volatility:
Upper and Lower Bands – Derived from ATR and band multipliers to expand/contract as volatility changes.
⚪ Retest Signal System
Optional signal markers show when price re-tests the upper or lower band:
Upper Retest → Pullback into resistance during a bearish trend.
Lower Retest → Pullback into support during a bullish trend.
⚪ Trend Step Signals
Circular markers can be shown to mark each time the trend steps forward, making it easy to identify structurally significant moments of continuation within a larger trend.
█ How to Use
⚪ Trend Alignment
Use the Trend Line and Step Markers to visually confirm the direction of momentum. If multiple trend steps occur in sequence without reversal, this typically signals strong conviction and trend persistence.
⚪ Retest-Based Entries
Wait for pullbacks into the channel and monitor for triangle retest signals. When used in confluence with trend direction, these offer high-quality continuation setups.
⚪ Breakouts
Look for breakouts beyond the upper or lower band after a longer period of pause. For higher likelihood of success, look for breakouts in the direction of the trend.
█ Settings
Trigger Threshold (× ATR) - Defines how far price must move to register a new trend step. Controls sensitivity to trend flips.
Max Step Size (× ATR) - Caps how far each trend step can extend. Prevents runaway step expansion in high volatility.
Band Multiplier (× ATR) - Expands the upper and lower channels. Controls how much breathing room the bands allow.
Trend Hold (bars) - Minimum number of bars the trend must remain active before allowing a flip. Helps reduce noise.
Filter by Trend - Restrict retest signals to those aligned with the current 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.
Bollinger Free BarsIdentify Extreme Price Actions with Non-Overlay Visualization
Core Functionality
This indicator detects two types of Bollinger Band breakout patterns without cluttering your chart:
1 ️⃣ Half Breakout Bar (Blue Triangles)
- Triggers when both open & close prices are outside the Bollinger Bands
- Suggests strong directional momentum continuation
2 ️⃣ Complete Breakout Bar (Red Flags)
- Activates when entire price action (including wicks) stays outside the bands
- Signals extreme volatility exhaustion points
Feature Highlights
◾ Smart Band Display
Translucent bands (#2196F3 & #FF9800 with 70% transparency) maintain chart clarity while showing dynamic volatility ranges
◾ Parameter Customization
- Adjustable period (default 20) & deviation multiplier (default 2.0)
- Selectable price source (close/open/high/low)
◾ Statistical Validation
Based on Bollinger Band's 95.4% price containment principle, signals filter out 4.6% extreme market conditions for high-probability scenarios.
Recommended Usage
1. Combine with volume analysis (significant breakout with high volume increases signal reliability)
2. Confirm with trend lines or RSI divergence
3. Adjust transparency via "Style" tab for multi-indicator layouts
Code Safety
- No repainting: All calculations use historical price data only
- No external network requests
- Open-source logic compliant with Pine Script v6 standards
Disclaimer
This tool is for technical analysis education only. Past performance doesn't guarantee future results. Always validate signals with fundamental analysis and proper risk management.
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
United HUN CityPurpose and Usage
The purpose of this strategy is to create a composite indicator that combines the signals from the MFI, Fisher Transform, and Bollinger Bands %b indicators. By normalizing and averaging these indicators, the script aims to provide a smoother and more comprehensive signal that can be used to make trading decisions.
MFI (Money Flow Index): Measures buying and selling pressure based on price and volume.
Fisher Transform: Highlights potential reversal points by transforming price data to a Gaussian normal distribution.
Bollinger Bands %b: Indicates where the price is relative to the Bollinger Bands, helping to identify overbought or oversold conditions.
The combined indicator can be used to identify potential buy or sell signals based on the smoothed composite value. For instance, a high combined indicator value might indicate overbought conditions, while a low value might indicate oversold conditions.
CME Gap Oscillator [CryptoSea]Introducing the CME Gap Oscillator , a pioneering tool designed to illuminate the significance of market gaps through the lens of the Chicago Mercantile Exchange (CME). By leveraging gap sizes in relation to the Average True Range (ATR), this indicator offers a unique perspective on market dynamics, particularly around the critical weekly close periods.
Key Features
Gap Measurement : At its core, the CME Oscillator quantifies the size of weekend gaps in the context of the market's volatility, using the ATR to standardize this measurement.
Dynamic Levels : Incorporating a dynamic extreme level calculation, the tool adapts to current market conditions, providing real-time insights into significant gap sizes and their implications.
Band Analysis : Through the introduction of upper and lower bands, based on standard deviations, traders can visually assess the oscillator's position relative to typical market ranges.
Enhanced Insights : A built-in table tracks the frequency of the oscillator's breaches beyond these bands within the latest CME week, offering a snapshot of recent market extremities.
Settings & Customisation
ATR-Based Measurement : Choose to measure gap sizes directly or in terms of ATR for a volatility-adjusted view.
Band Period Adjustability : Tailor the oscillator's sensitivity by modifying the band calculation period.
Dynamic Level Multipliers : Adjust the multiplier for dynamic levels to suit your analysis needs.
Visual Preferences : Customise the oscillator, bands, and table visuals, including color schemes and line styles.
In the example below, it demonstrates that the CME will want to return to the 0 value, this would be considered a reset or gap fill.
Application & Strategy
Deploy the CME Oscillator to enhance your market analysis
Market Sentiment : Gauge weekend market sentiment shifts through gap analysis, refining your strategy for the week ahead.
Volatility Insights : Use the oscillator's ATR-based measurements to understand the volatility context of gaps, aiding in risk management.
Trend Identification : Identify potential trend continuations or reversals based on the frequency and magnitude of gaps exceeding dynamic levels.
The CME Oscillator stands out as a strategic tool for traders focusing on gap analysis and volatility assessment. By offering a detailed breakdown of market gaps in relation to volatility, it empowers users with actionable insights, enabling more informed trading decisions across a range of markets and timeframes.
