BB next+2This indicator extends the standard Bollinger Bands by allowing you to project future Bollinger Bands based on assumed closing prices for the next trading day (+1) and the day after (+2).
Key Features:
Plots standard Bollinger Bands (supports SMA, EMA, etc.)
Allows manual input of assumed closing prices for the next trading day (+1) and the day after (+2)
Displays projected Bollinger Bands (basis, upper, and lower) based on the input values
Option to restrict display to the latest bar or confirmed bars only
ابحث في النصوص البرمجية عن "bands"
The Kyber Cell's – TTM Squeeze ProThe Kyber Cell’s TTM Squeeze Pro
TTM Squeeze + ALMA + VWAP for Precision Trade Timing
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1. Introduction
Kyber Cell’s Squeeze Pro is a comprehensive, all-in-one overlay indicator built on top of John Carter’s famous TTM Squeeze concept. It integrates advanced momentum and trend analysis using Arnaud Legoux Moving Averages (ALMA), a scroll-aware VWAP with optional deviation bands, and a clean, user-friendly visual system. The goal is simple: give traders a clear and configurable chart that identifies price compression, detects release moments, confirms direction, and helps manage risk and reward visually and effectively.
This tool is intended for traders of all styles — scalpers, swing traders, or intraday strategists — looking for cleaner signals, better visual cues, and more confidence in entry/exit timing.
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2. Core Concepts
At its heart, the Squeeze Pro builds an in-chart visualization of the TTM Squeeze, a strategy that identifies when price volatility compresses inside a Bollinger Band that is narrower than a Keltner Channel. These moments often precede explosive breakouts. This version categorizes squeezes into three levels of compression:
• Blue Dot – Low Compression
• Orange Dot – Medium Compression
• Red Dot – High Compression
When the squeeze “fires” (i.e., the Bollinger Bands expand beyond all Keltner thresholds), the indicator flips to a Green Dot, signaling potential entry if confirmed by trend direction.
The indicator also includes a momentum model using linear regression on smoothed price deviation to determine directional bias. Momentum is further reinforced by a customizable trend engine, allowing you to switch between EMA-21 or HMA 34/144 logic.
An ALMA ribbon is plotted across the chart to represent smoothed trend strength with minimal lag, and a scroll-aware VWAP (Volume-Weighted Average Price) line, optionally with ±σ bands, helps confirm mean-reversion or momentum continuation setups.
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3. Visual Components
Squeeze Pro replaces the traditional histogram with bar coloring logic based on your selected overlay mode:
• Momentum Mode colors bars based on whether momentum is rising or falling and in which direction (aqua/blue for bullish, red/yellow for bearish).
• Trend Mode colors bars using EMA or HMA logic to identify whether price is in a bullish, bearish, or neutral trend state.
A colored backdrop is triggered when a squeeze fires and momentum direction is confirmed. It remains green for bullish runs and red for bearish runs. The background disappears when the trend exhausts or reverses.
Each squeeze level (low, medium, high) is plotted as tiny dots above or below candles, with configurable colors. On the exact bar where the squeeze fires, the indicator optionally plots entry markers — either arrows or triangles — which can be placed with adjustable padding using ATR. These provide an at-a-glance signal of possible long or short entries.
EXPERIMENTAL : For risk and reward management, protective stop lines and limit targets can be toggled on. Stops are calculated using either recent swing highs/lows or a fixed ATR multiple, depending on user preference. Limit targets are calculated from entry price using ATR-based projections.
All colors are customizable.
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4. Multi-Timeframe Squeeze Panel
An optional MTF Squeeze Panel appears in the top-right corner of the chart, displaying the squeeze status across multiple timeframes — from 1-minute to Monthly. Each timeframe is color-coded:
• Red for High Compression
• Orange for Medium Compression
• Blue for Low Compression
• Yellow for Open/No Compression
This provides rapid context for whether multiple timeframes are simultaneously compressing (a common precursor to explosive moves), helping traders align higher- and lower-timeframe signals. Colors are customizable.
The MTF panel dynamically adjusts to chart space and only renders the selected intervals for clarity and performance.
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5. Inputs and Configuration Options
Squeeze Pro offers a rich configuration suite:
• Squeeze Settings: Control the Bollinger Band standard deviation, and three separate Keltner Channel multipliers (for low, medium, and high compression zones).
• ALMA Controls: Adjust the smoothing length, offset, and σ factor to control ribbon sensitivity.
• VWAP Options: Toggle VWAP on/off and optionally show ±σ bands for mean reversion signals.
• Entry Markers: Customize marker shape (arrow or triangle), size (tiny to huge), color, and padding using ATR multipliers.
• Stops and Targets:
• Choose between Swing High/Low or ATR-based stop logic.
• Define separate ATR lengths and multipliers for stops and targets.
• Independently toggle their visibility and color.
• Bar Coloring Mode: Select either Momentum or Trend logic for bar overlays.
• Trend Engine: Choose between EMA-21 or HMA 34/144 for identifying trend direction.
• Squeeze Dot Colors: Customize the colors for each compression level and release state.
• MTF Panel: Toggle visibility per timeframe — from 1m to Monthly.
This high degree of customization ensures that the indicator can adapt to nearly any trading style or preference.
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6. Trade Workflow Suggestions
To get the most out of this tool, traders can follow a consistent workflow:
1. Watch Dot Progression: Blue → Orange → Red indicates increasing compression and likelihood of breakout.
2. Enter on Green Dot: When the squeeze fires (green dot), confirm entry direction with bar color and backdrop.
3. Use Confirmation Tools:
• ALMA should slope in the trade direction.
• VWAP should support the price move or confirm expansion away from mean.
4. Manage Risk and Reward (experimental):
• Respect stop-loss placements (Swing/ATR).
• Use ATR-based limit targets if enabled.
5. Exit:
• Consider exiting when momentum crosses zero.
• Or exit when the background color disappears, signaling potential trend exhaustion.
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7. Alerts
Includes built-in alert conditions to notify you when a squeeze fires in either direction:
• “Squeeze Long”: Triggers when a green dot appears and momentum is bullish.
• “Squeeze Short”: Triggers when a green dot appears and momentum is bearish.
You can use these alerts for automation or to stay notified of new setups even when away from the screen.
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8. Disclaimer
This indicator is designed for educational purposes only and should not be interpreted as financial advice. Trading is inherently risky, and any decisions based on this tool should be made with full awareness of personal risk tolerance and capital exposure.
Squeeze & Breakout Confirmation StrategyThis strategy focuses on identifying periods of low volatility (Bollinger Band Squeeze) and then confirming the direction of the subsequent breakout with momentum, volume, and candle strength.
Concepts Applied: Bollinger Bands (Squeeze), RSI (Momentum), Market Volume (Conviction), Candle Size (Strength)
Buy Signal:
Bollinger Band Squeeze: Look for a period where the Bollinger Bands contract significantly, indicating low volatility and consolidation. The bands should be very close to the price action.
RSI Breakout: After the squeeze, wait for the price to break decisively above the upper Bollinger Band. Simultaneously, the RSI should break above 60 (or even 70), indicating strong bullish momentum.
Volume Surge: The breakout candle should be accompanied by a significant increase in trading volume, ideally above its recent average, confirming strong buying interest.
Strong Bullish Candle: The breakout candle itself should be a large, bullish candle (e.g., a strong green candle with a small upper wick or a bullish engulfing pattern), demonstrating buyer conviction.
Sell Signal (Short):
Bollinger Band Squeeze: Look for a period where the Bollinger Bands contract significantly.
RSI Breakdown: After the squeeze, wait for the price to break decisively below the lower Bollinger Band. Simultaneously, the RSI should break below 40 (or even 30), indicating strong bearish momentum.
Volume Surge: The breakdown candle should be accompanied by a significant increase in trading volume, ideally above its recent average, confirming strong selling interest.
Strong Bearish Candle: The breakdown candle itself should be a large, bearish candle (e.g., a strong red candle with a small lower wick or a bearish engulfing pattern), demonstrating seller conviction.
ka66: Triple Keltner Around SourceThis is an indicator-on-indicator which draws Keltner Bands (ATR Bands) around any selected Basis Source, instead of hardcoding a moving average, etc. This allows you to put bands around any sort of esoteric moving average of your choice, or even just around price data like OHLC, HLC3, and so on.
It's an enhancement on my prior Multi ATR Channels script at
Written in Pine v6 and allowing custom timeframe selection.
For example, the published chart shows the bands place around a Kaufman Adaptive Moving Average (KAMA), plotted in blue dots.
You would use it for anything that you would use plain Keltners for:
Mean Reversion
Breakouts
Take Profit and Stop Loss Estimation
But with any basis that you deem more suitable for your purposes.
Smart Fib StrategySmart Fibonacci Strategy
This advanced trading strategy combines the power of adaptive SMA entries with Fibonacci-based exit levels to create a comprehensive trend-following system that self-optimizes based on historical market conditions. Credit goes to Julien_Eche who created the "Best SMA Finder" which received an Editors Pick award.
Strategy Overview
The Smart Fibonacci Strategy employs a two-pronged approach to trading:
1. Intelligent Entries: Uses a self-optimizing SMA (Simple Moving Average) to identify optimal entry points. The system automatically tests multiple SMA lengths against historical data to determine which period provides the most robust trading signals.
2. Fibonacci-Based Exits: Implements ATR-adjusted Fibonacci bands to establish precise exit targets, with risk-management options ranging from conservative to aggressive.
This dual methodology creates a balanced system that adapts to changing market conditions while providing clear visual reference points for trade management.
Key Features
- **Self-Optimizing Entries**: Automatically calculates the most profitable SMA length based on historical performance
- **Adjustable Risk Parameters**: Choose between low-risk and high-risk exit targets
- **Directional Flexibility**: Trade long-only, short-only, or both directions
- **Visualization Tools**: Customizable display of entry lines and exit bands
- **Performance Statistics**: Comprehensive stats table showing key metrics
- **Smoothing Option**: Reduces noise in the Fibonacci bands for cleaner signals
Trading Rules
Entry Signals
- **Long Entry**: When price crosses above the blue center line (optimal SMA)
- **Short Entry**: When price crosses below the blue center line (optimal SMA)
### Exit Levels
- **Low Risk Option**: Exit at the first Fibonacci band (1.618 * ATR)
- **High Risk Option**: Exit at the second Fibonacci band (2.618 * ATR)
Strategy Parameters
Display Settings
- Toggle visibility of the stats table and indicator components
Strategy Settings
- Select trading direction (long, short, or both)
- Choose exit method (low risk or high risk)
- Set minimum trades threshold for SMA optimization
SMA Settings
- Option to use auto-optimized or fixed-length SMA
- Customize SMA length when using fixed option
Fibonacci Settings
- Adjust ATR period and SMA basis for Fibonacci bands
- Enable/disable smoothing function
- Customize Fibonacci ratio multipliers
Appearance Settings
- Modify colors, line widths, and transparency
Optimization Methodology
The strategy employs a sophisticated optimization algorithm that:
1. Tests multiple SMA lengths against historical data
2. Evaluates performance based on trade count, profit factor, and win rate
3. Calculates a "robustness score" that balances profitability with statistical significance
4. Selects the SMA length with the highest robustness score
This ensures that the strategy's entry signals are continuously adapting to the most effective parameters for current market conditions.
Risk Management
Position sizing is fixed at $2,000 per trade, allowing for consistent exposure across all trading setups. The Fibonacci-based exit system provides two distinct risk management approaches:
- **Conservative Approach**: Using the first Fibonacci band for exits produces more frequent but smaller wins
- **Aggressive Approach**: Using the second Fibonacci band allows for larger potential gains at the cost of increased volatility
Ideal Usage
This strategy is best suited for:
- Trending markets with clear directional moves
- Timeframes from 4H to Daily for most balanced results
- Instruments with moderate volatility (stocks, forex, commodities)
Traders can further enhance performance by combining this strategy with broader market analysis to confirm the prevailing trend direction.
Volumetric Entropy IndexVolumetric Entropy Index (VEI)
A volume-based drift analyzer that captures directional pressure, trend agreement, and entropy structure using smoothed volume flows.
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🧠 What It Does:
• Volume Drift EMAs : Shows buy/sell pressure momentum with adaptive smoothing.
• Dynamic Bands : Bollinger-style volatility wrappers react to expanding/contracting drift.
• Baseline Envelope : Clean structural white rails for mean-reversion zones or trend momentum.
• Background Shading : Highlights when both sides (up & down drift) are in agreement — green for bullish, red for bearish.
• Alerts Included : Drift alignment, crossover events, net drift shifts, and strength spikes.
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🔍 What Makes It Different:
• Most volume indicators rely on bars, oscillators, or OBV-style accumulation — this doesn’t.
• It compares directional EMAs of raw volume to isolate real-time bias and acceleration.
• It visualizes the twisting tension between volume forces — not just price reaction.
• Designed to show when volatility is building inside the volume mechanics before price follows.
• Modular — every element is optional, so you can run it lean or fully loaded.
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📊 How to Use It:
• Drift EMAs : Watch for one side consistently dominating — sharp spikes often precede breakouts.
• Bands : When they tighten and start expanding, it often signals directional momentum forming.
• Envelope Lines : Use as high-probability reversal or continuation zones. Bands crossing envelopes = potential thrust.
• Background Color : Green/red backgrounds confirm volume agreement. Can be used as a filter for other signals.
• Net Drift : Optional smoothed oscillator showing the difference between bullish and bearish volume pressure. Crosses above or below zero signal directional bias shifts.
• Drift Strength : Measures pressure buildup — spikes often correlate with large moves.
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⚙️ Full Customization:
• Turn every layer on/off independently
• Modify all colors, transparencies, and line widths
• Adjust band width multiplier and envelope offset (%)
• Toggle bonus plots like drift strength and net baseline
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🧪 Experimental Tools:
• Smoothed Net Drift trace
• Drift Strength signal
• Envelope lines and dynamic entropy bands with adjustable math
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Built for signal refinement. Made to expose directional imbalance before the herd sees it.
Created by @Sherlock_Macgyver
External Signals Strategy Tester v5External Signals Strategy Tester v5 – User Guide (English)
1. Purpose
This Pine Script strategy is a universal back‑tester that lets you plug in any external buy/sell series (for example, another indicator, webhook feed, or higher‑time‑frame condition) and evaluate a rich set of money‑management rules around it – with a single click on/off workflow for every module.
2. Core Workflow
Feed signals
Buy Signal / Sell Signal inputs accept any series (price, boolean, output of request.security(), etc.).
A crossover above 0 is treated as “signal fired”.
Date filter
Start Date / End Date restricts the test window so you can exclude unwanted history.
Trade engine
Optional Long / Short enable toggles.
Choose whether opposite signals simply close the trade or reverse it (flip direction in one transaction).
Risk modules – all opt‑in via check‑boxes
Classic % block – fixed % Take‑Profit / Stop‑Loss / Break‑Even.
Fibonacci Bollinger Bands (FBB) module
Draws dynamic VWMA/HMA/SMA/EMA/DEMA/TEMA mid‑line with ATR‑scaled Fibonacci envelopes.
Every line can be used for stops, trailing, or multi‑target exits.
Separate LONG and SHORT sub‑modules
Each has its own SL plus three Take‑Profits (TP1‑TP3).
Per TP you set line, position‑percentage to close, and an optional trailing flag.
Executed TP/SLs deactivate themselves so they cannot refire.
Trailing behaviour
If Trail is checked, the selected line is re‑evaluated once per bar; the order is amended via strategy.exit().
3. Inputs Overview
Group Parameter Notes
Trade Settings Enable Long / Enable Short Master switches
Close on Opposite / Reverse Position How to react to a counter‑signal
Risk % Use TP / SL / BE + their % Traditional fixed‑distance management
Fibo Bands FIBO LEVELS ENABLE + visual style/length Turn indicator overlay on/off
FBB LONG SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a long is open
FBB SHORT SL / TP1‑TP3 Enable, Line, %, Trail Rules applied only while a short is open
Line choices: Basis, 0.236, 0.382, 0.5, 0.618, 0.764, 1.0 – long rules use lower bands, short rules use upper bands automatically.
4. Algorithm Details
Position open
On the very first bar after entry, the script checks the direction and activates the corresponding LONG or SHORT module, deactivating the other.
Order management loop (every bar)
FBB Stop‑Loss: placed/updated at chosen band; if trailing, follows the new value.
TP1‑TP3: each active target updates its limit price to the selected band (or holds static if trailing is off).
The classic % block runs in parallel; its exits have priority because they call strategy.close_all().
Exit handling
When any strategy.exit() fires, the script reads exit_id and flips the *_Active flag so that order will not be recreated.
A Stop‑Loss (SL) also disables all remaining TPs for that leg.
5. Typical Use Cases
Scenario Suggested Setup
Scalping longs into VWAP‐reversion Enable LONG TP1 @ 0.382 (30 %), TP2 @ 0.618 (40 %), SL @ 0.236 + trailing
Fade shorts during news spikes Enable SHORT SL @ 1.0 (no trail) and SHORT TP1,2,3 on consecutive lowers with small size‑outs
Classic trend‑follow Use only classic % TP/SL block and disable FBB modules
6. Hints & Tips
Signal quality matters – this script manages exits, it does not generate entries.
Keep TV time zone in mind when picking start/end dates.
For portfolio‑style testing allocate smaller default_qty_value than 100 % or use strategy.percent_of_equity sizing.
You can combine FBB exits with fixed‑% ones for layered management.
7. Limitations / Safety
No pyramiding; the script holds max one position at a time.
All calculations are bar‑close; intra‑bar touches may differ from real‑time execution.
The indicator overlay is optional, so you can run visual‑clean tests by unchecking FIBO LEVELS ENABLE.
Multi-Anchored Linear Regression Channels [TANHEF]█ Overview:
The 'Multi-Anchored Linear Regression Channels ' plots multiple dynamic regression channels (or bands) with unique selectable calculation types for both regression and deviation. It leverages a variety of techniques, customizable anchor sources to determine regression lengths, and user-defined criteria to highlight potential opportunities.
Before getting started, it's worth exploring all sections, but make sure to review the Setup & Configuration section in particular. It covers key parameters like anchor type, regression length, bias, and signal criteria—essential for aligning the tool with your trading strategy.
█ Key Features:
⯁ Multi-Regression Capability:
Plot up to three distinct regression channels and/or bands simultaneously, each with customizable anchor types to define their length.
⯁ Regression & Deviation Methods:
Regressions Types:
Standard: Uses ordinary least squares to compute a simple linear trend by averaging the data and deriving a slope and endpoints over the lookback period.
Ridge: Introduces L2 regularization to stabilize the slope by penalizing large coefficients, which helps mitigate multicollinearity in the data.
Lasso: Uses L1 regularization through soft-thresholding to shrink less important coefficients, yielding a simpler model that highlights key trends.
Elastic Net: Combines L1 and L2 penalties to balance coefficient shrinkage and selection, producing a robust weighted slope that handles redundant predictors.
Huber: Implements the Huber loss with iteratively reweighted least squares (IRLS) and EMA-style weights to reduce the impact of outliers while estimating the slope.
Least Absolute Deviations (LAD): Reduces absolute errors using iteratively reweighted least squares (IRLS), yielding a slope less sensitive to outliers than squared-error methods.
Bayesian Linear: Merges prior beliefs with weighted data through Bayesian updating, balancing the prior slope with data evidence to derive a probabilistic trend.
Deviation Types:
Regressive Linear (Reverse): In reverse order (recent to oldest), compute weighted squared differences between the data and a line defined by a starting value and slope.
Progressive Linear (Forward): In forward order (oldest to recent), compute weighted squared differences between the data and a line defined by a starting value and slope.
Balanced Linear: In forward order (oldest to newest), compute regression, then pair to source data in reverse order (newest to oldest) to compute weighted squared differences.
Mean Absolute: Compute weighted absolute differences between each data point and its regression line value, then aggregate them to yield an average deviation.
Median Absolute: Determine the weighted median of the absolute differences between each data point and its regression line value to capture the central tendency of deviations.
Percent: Compute deviation as a percentage of a base value by multiplying that base by the specified percentage, yielding symmetric positive and negative deviations.
Fitted: Compare a regression line with high and low series values by computing weighted differences to determine the maximum upward and downward deviations.
Average True Range: Iteratively compute the weighted average of absolute differences between the data and its regression line to yield an ATR-style deviation measure.
Bias:
Bias: Applies EMA or inverse-EMA style weighting to both Regression and/or Deviation, emphasizing either recent or older data.
⯁ Customizable Regression Length via Anchors:
Anchor Types:
Fixed: Length.
Bar-Based: Bar Highest/Lowest, Volume Highest/Lowest, Spread Highest/Lowest.
Correlation: R Zero, R Highest, R Lowest, R Absolute.
Slope: Slope Zero, Slope Highest, Slope Lowest, Slope Absolute.
Indicator-Based: Indicators Highest/Lowest (ADX, ATR, BBW, CCI, MACD, RSI, Stoch).
Time-Based: Time (Day, Week, Month, Quarter, Year, Decade, Custom).
Session-Based: Session (Tokyo, London, New York, Sydney, Custom).
Event-Based: Earnings, Dividends, Splits.
External: Input Source Highest/Lowest.
Length Selection:
Maximum: The highest allowed regression length (also fixed value of “Length” anchor).
Minimum: The shortest allowed length, ensuring enough bars for a valid regression.
Step: The sampling interval (e.g., 1 checks every bar, 2 checks every other bar, etc.). Increasing the step reduces the loading time, most applicable to “Slope” and “R” anchors.
Adaptive lookback:
Adaptive Lookback: Enable to display regression regardless of too few historical bars.
⯁ Selecting Bias:
Bias applies separately to regression and deviation.
Positive values emphasize recent data (EMA-style), negative invert, and near-zero maintains balance. (e.g., a length 100, bias +1 gives the newest price ~7× more weight than the oldest).
It's best to apply bias to both (regression and deviation) or just the deviation. Biasing only regression may distort deviation visually, while biasing both keeps their relationship intuitive. Using bias only for deviation scales it without altering regression, offering unique analysis.
⯁ Scale Awareness:
Supports linear and logarithmic price scaling, the regression and deviations adjust accordingly.
⯁ Signal Generation & Alerts:
Customizable entry/exit signals and alerts, detailed in the dedicated section below.
⯁ Visual Enhancements & Real-World Examples:
Optional on-chart table display summarizing regression input criteria (display type, anchor type, source, regression type, regression bias, deviation type, deviation bias, deviation multiplier) and key calculated metrics (regression length, slope, Pearson’s R, percentage position within deviations, etc.) for quick reference.
█ Understanding R (Pearson Correlation Coefficient):
Pearson’s R gauges data alignment to a straight-line trend within the regression length:
Range: R varies between –1 and +1.
R = +1 → Perfect positive correlation (strong uptrend).
R = 0 → No linear relationship detected.
R = –1 → Perfect negative correlation (strong downtrend).
This script uses Pearson’s R as an anchor, adjusting regression length to target specific R traits. Strong R (±1) follows the regression channel, while weak R (0) shows inconsistency.
█ Understanding the Slope:
The slope is the direction and rate at which the regression line rises or falls per bar:
Positive Slope (>0): Uptrend – Steeper means faster increase.
Negative Slope (<0): Downtrend – Steeper means sharper drop.
Zero or Near-Zero Slope: Sideways – Indicating range-bound conditions.
This script uses highest and lowest slope as an anchor, where extremes highlight strong moves and trend lines, while values near zero indicate sideways action and possible support/resistance.
█ Setup & Configuration:
Whether you’re new to this script or want to quickly adjust all critical parameters, the panel below shows the main settings available. You can customize everything from the anchor type and maximum length to the bias, signal conditions, and more.
Scale (select Log Scale for logarithmic, otherwise linear scale).
Display (regression channel and/or bands).
Anchor (how regression length is determined).
Length (control bars analyzed):
• Max – Upper limit.
• Min – Prevents regression from becoming too short.
• Step – Controls scanning precision; increasing Step reduces load time.
Regression:
• Type – Calculation method.
• Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more).
Deviation:
• Type – Calculation method.
• Bias – EMA-style emphasis (>0=new bars weighted more; <0=old bars weighted more).
• Multiplier - Adjusts Upper and Lower Deviation.
Signal Criteria:
• % (Price vs Deviation) – (0% = lower deviation, 50% = regression, 100% = upper deviation).
• R – (0 = no correlation, ±1 = perfect correlation; >0 = +slope, <0 = -slope).
Table (analyze table of input settings, calculated results, and signal criteria).
Adaptive Lookback (display regression while too few historical bars).
Multiple Regressions (steps 2 to 7 apply to #1, #2, and #3 regressions).
█ Signal Generation & Alerts:
The script offers customizable entry and exit signals with flexible criteria and visual cues (background color, dots, or triangles). Alerts can also be triggered for these opportunities.
Percent Direction Criteria:
(0% = lower deviation, 50% = regression line, 100% = upper deviation)
Above %: Triggers if price is above a specified percent of the deviation channel.
Below %: Triggers if price is below a specified percent of the deviation channel.
(Blank): Ignores the percent‐based condition.
Pearson's R (Correlation) Direction Criteria:
(0 = no correlation, ±1 = perfect correlation; >0 = positive slope, <0 = negative slope)
Above R / Below R: Compares the correlation to a threshold.
Above│R│ / Below│R│: Uses absolute correlation to focus on strength, ignoring direction.
Zero to R: Checks if R is in the 0-to-threshold range.
(Blank): Ignores correlation-based conditions.
█ User Tips & Best Practices:
Choose an anchor type that suits your strategy, “Bar Highest/Lowest” automatically spots commonly used regression zones, while “│R│ Highest” targets strong linear trends.
Consider enabling or disabling the Adaptive Lookback feature to ensure you always have a plotted regression if your chart doesn’t meet the maximum-length requirement.
Use a small Step size (1) unless relying on R-correlation or slope-based anchors as the are time-consuming to calculate. Larger steps speed up calculations but reduce precision.
Fine-tune settings such as lookback periods, regression bias, and deviation multipliers, or trend strength. Small adjustments can significantly affect how channels and signals behave.
To reduce loading time , show only channels (not bands) and disable signals, this limits calculations to the last bar and supports more extreme criteria.
Use the table display to monitor anchor type, calculated length, slope, R value, and percent location at a glance—especially if you have multiple regressions visible simultaneously.
█ Conclusion:
With its blend of advanced regression techniques, flexible deviation options, and a wide range of anchor types, this indicator offers a highly adaptable linear regression channeling system. Whether you're anchoring to time, price extremes, correlation, slope, or external events, the tool can be shaped to fit a variety of strategies. Combined with customizable signals and alerts, it may help highlight areas of confluence and support a more structured approach to identifying potential opportunities.
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.
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 .
⸻
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.
⸻
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.
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
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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
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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
