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.
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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.
Keltner Hull Suite [QuantAlgo]🟢 Overview
The Keltner Hull Suite combines Hull Moving Average positioning with double-smoothed True Range banding to identify trend regimes and filter market noise. The indicator establishes upper and lower volatility bounds around the Hull MA, with the trend line conditionally updating only when price violates these boundaries. This mechanism distinguishes between genuine directional shifts and temporary price fluctuations, providing traders and investors with a systematic framework for trend identification that adapts to changing volatility conditions across multiple timeframes and asset classes.
🟢 How It Works
The calculation foundation begins with the Hull Moving Average, a weighted moving average designed to minimize lag while maintaining smoothness:
hullMA = ta.hma(priceSource, hullPeriod)
The indicator then calculates true range and applies dual exponential smoothing to create a volatility measure that responds more quickly to volatility changes than traditional ATR implementations while maintaining stability through the double-smoothing process:
tr = ta.tr(true)
smoothTR = ta.ema(tr, keltnerPeriod)
doubleSmooth = ta.ema(smoothTR, keltnerPeriod)
deviation = doubleSmooth * keltnerMultiplier
Dynamic support and resistance boundaries are constructed by applying the multiplier-scaled volatility deviation to the Hull MA, creating upper and lower bounds that expand during volatile periods and contract during consolidation:
upperBound = hullMA + deviation
lowerBound = hullMA - deviation
The trend line employs a conditional update mechanism that prevents premature trend reversals. The system maintains the current trend line until price action violates the respective boundary, at which point the trend line snaps to the violated bound:
if upperBound < trendLine
trendLine := upperBound
if lowerBound > trendLine
trendLine := lowerBound
Directional bias determination compares the current trend line value against its previous value, establishing bullish conditions when rising and bearish conditions when falling. Signal generation occurs on state transitions, triggering alerts when the trend state shifts from neutral or opposite direction:
trendUp = trendLine > trendLine
trendDown = trendLine < trendLine
longSignal = trendState == 1 and trendState != 1
shortSignal = trendState == -1 and trendState != -1
The visualization layer creates a trend band by plotting both the current trend line and a two-bar shifted version, with the area between them filled to create a visual channel that reinforces directional conviction.
🟢 How to Use This Indicator
▶ Long and Short Signals: The indicator generates long/buy signals when the trend state transitions to bullish (trend line begins rising) and short/sell signals when transitioning to bearish (trend line begins falling). These state changes represent structural shifts in momentum where price has broken through the adaptive volatility bands, confirming directional commitment.
▶ Trend Band Dynamics: The spacing between the main trend line and its shifted counterpart creates a visual band whose width reflects trend strength and momentum consistency. Expanding bands indicate accelerating directional movement and strong trend persistence, while contracting or flattening bands suggest decelerating momentum, potential trend exhaustion, or impending consolidation. Monitoring band width provides early warning of regime transitions from trending to range-bound conditions.
▶ Preconfigured Presets: Three optimized parameter sets accommodate different trading styles and timeframes. Default (14, 20, 2.0) provides balanced trend identification suitable for daily charts and swing trading, Fast Response (10, 14, 1.5) delivers aggressive signal generation optimized for intraday scalping and momentum trading on 1-15 minute timeframes, while Smooth Trend (18, 30, 2.5) offers conservative trend confirmation ideal for position trading on 4-hour to daily charts with enhanced noise filtration.
▶ Built-in Alerts: Three alert conditions enable automated monitoring - Bullish Trend Signal triggers on long setup confirmation, Bearish Trend Signal activates on short setup confirmation, and Trend Change alerts on any directional transition. These notifications allow you to respond to regime shifts without continuous chart monitoring.
▶ Color Customization: Five visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and display preferences, ensuring optimal contrast and visual clarity across trading environments.
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current NQ price and VIX level
Daily Open
Expected move
NQ-VIX Expected Move LTF LevelsNQ -VIX LTF Price Bands
This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (NQ Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current NQ price and VIX level
Current input TF Open
Expected move
ES-VIX Expected Move LTF LevelsES-VIX LTF Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = (Input TF Open) + (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
Lower Band = Daily Open - (ES Price × VIX x √(Input TF ÷ (23h in min) ) ÷ 100
The calculation uses the square root of Input TF ÷ (23h in min) to convert annualized VIX volatility into an expected TF move, then scales it as a percentage adjustment from the current TF input's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current TF's open
Lower band (red) contracts from the current TF's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current input TF
Current ES price and VIX level
Current input TF Open
Expected move
ES-VIX Expected Move - Open basedES-VIX Daily Price Bands
This indicator plots dynamic intraday price bands for ES futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets.
Formulas:
Upper Band = Daily Open + (ES Price × VIX ÷ √252 ÷ 100)
Lower Band = Daily Open - (ES Price × VIX ÷ √252 ÷ 100)
The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open.
Features:
Real-time band calculation that updates throughout the trading session
Upper band (green) extends from the current day's open
Lower band (red) contracts from the current day's open
Inner upper band (green) at 50% of expected move
Inner lower band (red) at 50% of expected move
Middle Inner upper band (green) at 80% of expected move
Middle Inner lower band (red) at 80% of expected move
Information table displaying:
Current ES price and VIX level
Daily Open
Expected move
Buy/Sell Signals [WynTrader]Hello dear Friend
Here is a new version ( B-S_251121_wt ) of my Buy/Sell Signals indicator.
Some calculation updates and useful enhancements have been applied.
Concepts
This Buy/Sell Signals indicator generates Buy/Sell signals as accurately as possible, identifying trend changes. Compared to other tools that detect trend shifts, this one is simple, easy to use, and demonstrates its efficiency on its own.
- Its features are carefully designed to minimize false signals while ensuring optimal signal placement.
- The Table results allow you to quickly evaluate signal performance, both on their own and compared to a Buy & Hold strategy.
- The Table calculations are fully synchronized with the visible chart (WYSIWYG – What You See Is What You Get). You can also scroll the chart across different date ranges to see how a stock or product performs under various market conditions.
- Seeing Buy/Sell signals on a chart is appealing, but assessing their performance in a Table makes it even more convincing. And without running a full backtest, you can get a clear overview of overall performance immediately.
Features
This indicator generates Buy/Sell signals using:
- Fast and Slow Moving Averages (adjustable).
- Bollinger Bands (adjustable).
- Filters (optional, adjustable) to refine signals, including : Bollinger Bands Lookback Trend Filter; High-Low vs Candle Range Threshold %; Distance from Fast and Slow MAs Threshold %.
- Results are displayed in a Table on the chart, based on the currently visible start and end dates.
Functionality
- The indicator aims to confirm trend changes through timely Buy/Sell signals.
- It uses two Moving Averages and Bollinger Bands, combined with filters such as BB Lookback, -- The variable settings have been tested with a mix of manual and AI testing to find the optimal configuration. You can adjust the variables to suit your goals.
- The design is simple, with clear parameters and instant readability of Buy/Sell Signals on the chart and in the Table results, without complex interpretation needed.
- It works effectively by requiring both trend confirmation and volatility control management.
- Signals are timed to be as accurate as possible, avoiding futile weak or false ones.
- A Table shows the effectiveness of the signals on the current visible chart, providing immediate, realistic feedback. The Buy & Hold strategy results are also included for comparison with the Buy/Sell swing strategy. The Buy & Hold results start from the first Buy signal to ensure a fair comparison.
- Changing the parameters instantly updates the Table, giving a quick, at-a-glance performance check.
Caution
- No technical tool is perfect; it cannot predict disasters, wars, or the actions of large fund managers or short sellers.
- After testing thousands of TradingView indicators over 24 years, I’ve found none to be 100% accurate all the time.
- This Buy/Sell Signals indicator may outperform some others but is still not perfect.
So, just be aware, and don’t be fooled by this tool.
Grok/Claude AI Regime Engine • Grok/Claude X SeriesGrok/Claude AI Regime Engine
This is a TradingView indicator designed to identify market regimes (bullish, bearish, or neutral) and generate buy/sell signals based on multiple technical factors working together.
Core Concept
At its heart, this indicator tries to answer a simple question: "What kind of market are we in right now, and when should I consider buying or selling?"
It does this by blending several well-known technical analysis tools into a unified system. Think of it as a dashboard that synthesizes multiple indicators into clear, actionable information.
How It Determines Market Regime
The indicator creates what it calls a "Money Line" by combining two exponential moving averages (EMAs) — a fast one (default 8 periods) and a slow one (default 24 periods). These are weighted together, with the fast EMA getting 60% influence by default. This blended line serves as the primary trend reference.
Bullish regime is declared when the short EMA crosses above the long EMA, provided the RSI isn't already in overbought territory. Bearish regime kicks in when the opposite happens — short EMA crosses below long, as long as RSI isn't oversold. Neutral regime occurs when the indicator detects sideways, choppy conditions.
The neutral detection is particularly interesting. It uses two optional methods: one looks at how flat the Money Line's slope is (compared to recent volatility via ATR), and the other checks how close together the two EMAs are as a percentage of price. When the market is grinding sideways, these methods help the indicator avoid falsely calling a trend.
Signal Generation Logic
Buy and sell signals are generated using Donchian Channel breakouts as the trigger mechanism. The Donchian Channel tracks the highest high and lowest low over a lookback period (default 20 bars), using the previous bar's values to avoid repainting issues.
A buy signal fires when price touches or breaks below the lower Donchian band, suggesting a potential reversal from oversold conditions. A sell signal fires when price reaches the upper band. However, these raw breakout signals pass through several filters before being displayed:
FilterPurposeADX thresholdOnly signals when the market has sufficient trend strength (default: ADX > 25)RSI filterBuy signals require RSI to be oversold; sell signals require overbought RSICooldown periodPrevents signal spam by requiring a minimum number of bars between signalsClose confirmationOptional setting to require a candle close beyond the band, not just a wick
Additional Metrics Displayed
The indicator calculates and displays several supplementary metrics in an information panel. ADX (Average Directional Index) measures trend strength — values below 15 suggest a weak, ranging market, while above 25 indicates a strong trend. The colored dots at the bottom of the chart reflect this: white for weak, orange for moderate, blue for strong.
BBWP (Bollinger Band Width Percentile) measures current volatility relative to historical volatility over roughly a year of data. High readings suggest volatility expansion; low readings suggest compression, which often precedes significant moves.
Alerts and Notifications
The indicator generates alerts in two scenarios: when the market regime changes (bullish to bearish, etc.) and when buy/sell signals trigger. Alert messages include the ticker symbol, timeframe, current price, RSI, ADX, and other relevant context so you can quickly assess the situation without opening the chart.
Visual Customization
Users can toggle various display elements on or off, including the EMA lines, Donchian bands, shaded regime zones between the bands, and price labels at signal points. The shading between the upper and lower bands changes color based on the current regime — green for bullish, magenta for bearish, and blue for neutral — providing an at-a-glance view of market conditions over time.
Summary
This is essentially a trend-following system with mean-reversion entry signals, filtered by momentum and trend strength indicators. It's designed to help traders identify favorable market conditions and time entries while avoiding signals during choppy, directionless periods. The multiple confirmation layers aim to reduce false signals, though like any technical system, it will still produce losing trades in certain market conditions.
Bollinger Band Width Oscillator %🧠 Bollinger Band Width Oscillator %
The Bollinger Band Width Oscillator % is a volatility-focused tool that measures the relative width of Bollinger Bands and transforms it into an oscillator format. It helps traders visualize volatility expansions and contractions directly in an indicator pane — a powerful way to anticipate breakout or consolidation phases.
🔍 How It Works
Band Width %: Calculates the percentage distance between the upper and lower Bollinger Bands relative to the basis (SMA).
Smoothed Output: The raw bandwidth is smoothed using a moving average for cleaner, more stable signals.
Dynamic Volatility Zones: The script automatically computes average, high, and low volatility thresholds — each dynamically adapting to market conditions.
User-Adjustable Multipliers: Control how sensitive your high/low zones are with the High Zone Multiplier and Low Zone Multiplier inputs.
⚙️ Key Features
📊 Oscillator Format – Easy-to-read visualization of volatility compression and expansion.
🔥 High/Low Volatility Detection – Automatic labeling and color-coded alerts for shifts in volatility.
🧩 Dynamic Thresholds – Zones adjust automatically with market activity for adaptive sensitivity.
🧠 Hysteresis Logic – Prevents rapid signal flipping, improving clarity and reliability.
🎨 Custom Visuals – Adjustable smoothing and background highlights for quick interpretation.
📈 Trading Applications
Identify Breakouts: Rising bandwidth often precedes price breakouts.
Spot Consolidations: Low bandwidth indicates tightening volatility and potential range trades.
Volatility Regime Analysis: Understand market rhythm and adapt strategies accordingly.
⚡ Inputs
Parameter Description
Band Length Period for Bollinger Band calculation
Band Multiplier Standard deviation multiplier for the bands
Source Price source (default: close)
Smoothing Period for smoothing the oscillator line
High Zone Multiplier Adjusts the high-volatility threshold
Low Zone Multiplier Adjusts the low-volatility threshold
Highlight Volatility Zones Optional background color overlay
🧊 Usage Tip
Combine this indicator with momentum tools or price action analysis to confirm trade setups. Watch for transitions from low to high volatility zones — these often signal the beginning of major market moves.
Blue Dot Red DotInspired by Dr Wish
This script is a confluence indicator designed to identify potential trend reversals or "mean reversion" trade setups. It plots buy (blue) and sell (red) dots directly on your price chart.
The core strategy is to find moments where price is overextended (using Bollinger Bands) and momentum is simultaneously reversing (using the Stochastic Oscillator). A signal is only generated when both of these conditions are met.
Core Components
The script combines two classic technical indicators:
Bollinger Bands (BB):
These create a "channel" around the price based on a simple moving average (the basis) and a standard deviation (dev).
Upper Band: Basis + (2.0 * StdDev)
Lower Band: Basis - (2.0 * StdDev)
In this script, the bands are used to identify when the price has moved significantly far from its recent average, suggesting it's "overbought" (at the upper band) or "oversold" (at the lower band) and may be due for a pullback.
Stochastic Oscillator:
This is a momentum oscillator that compares a closing price to its price range over a certain period.
It consists of two lines: %K (the main, faster line) and %D (a moving average of %K, the slower signal line).
It's used to identify overbought and oversold momentum conditions and, more importantly, momentum shifts, which are signaled by the %K and %D lines crossing.
Signal Logic: How the Dots Are Generated
This script's "secret sauce" is that it demands three specific conditions to be true at the same time before plotting a dot.
🔵 Blue Dot (Buy Signal)
A blue dot will appear below a price bar if all three of these conditions are met:
Stochastic Crossover: The faster %K line crosses above the slower %D line (ta.crossover(k, d)). This signals that short-term momentum is starting to turn bullish.
Was Oversold: On the previous bar, the %K line was below the "Oversold Threshold" (was_oversold = k < oversold). This ensures the bullish crossover is happening from an oversold (or at least bearish) momentum state.
Note: The default oversold threshold is set to 50. This is a key detail. It means the script is looking for a bullish crossover that originates from anywhere in the bottom half of the Stochastic range, not just the traditional "extreme" oversold area (like 20).
Price Extension: Within the last 3 bars (the current bar or the two before it), the price's low must have touched or gone below the lower Bollinger Band (bb_touch_lower). This confirms that the price itself is in an "oversold" or overextended area.
In plain English: A blue dot appears when the price has recently dipped to an extreme low (touching the lower BB) and its underlying momentum has just started to turn back up (Stoch cross from the lower half).
🔴 Red Dot (Sell Signal)
A red dot will appear above a price bar if all three of these conditions are met:
Stochastic Crossunder: The faster %K line crosses below the slower %D line (ta.crossunder(k, d)). This signals that short-term momentum is starting to turn bearish.
Was Overbought: On the previous bar, the %K line was above the "Overbought Threshold" (was_overbought = k > overbought). The default for this is 80, which is a traditional overbought level.
Price Extension: Within the last 3 bars (the current bar or the two before it), the price's high must have touched or gone above the upper Bollinger Band (bb_touch_upper). This confirms that the price itself is in an "overbought" or overextended area.
A red dot appears when the price has recently spiked to an extreme high (touching the upper BB) and its underlying momentum has just started to roll over and turn back down (Stoch cross from the overbought zone).
CloudShiftCloudShift + Bollinger Bands
This version of CloudShift now includes fully optimized Bollinger Bands with all three dynamic lines:
Upper Band: Highlights expansion during volatility spikes.
Lower Band: Identifies compression and accumulation zones.
Centerline (Basis): A smooth reference of the moving average, providing better visual balance and directional context.
The bands are drawn with thin, clean lime lines, designed to integrate perfectly with the cloud logic — keeping your chart minimalist yet powerful.
This update enhances the CloudShift indicator by providing a clear visual framework of market volatility and structure without altering its original logic.
Recommended for use on: NASDAQ, S&P 500, and other high-volatility futures.
Recommended timeframe: 5–15 minutes.
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
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🔍 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 .
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⚙️ Core Features
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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.
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🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
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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.
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2. Use Compression Heat to Anticipate Breakouts
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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.
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3. Leverage the Optional Table UI
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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.
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4. Analyze Candle Color for Composite Signals
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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.
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🧰 Configuration Guidance
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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.
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✅ Best Practices
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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.
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📌 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.
