GapologyThis indicator can be used as a simple measure of price action tradability. It's an alternative to volume that focuses on the gaps between close and open candle prices. The bigger the gaps, the more spread and slippage you'll get when trading.
Cari dalam skrip untuk "gaps"
Hersheys Volume Pressure v2Hersheys Volume Pressure gives you very nice confirmation of trend starts and stops using volume and price.
For up bars...
If you have a large price change with low volume , that's very bullish .
If you have a small price change with low volume , that's bullish .
For down bars...
If you have a large price change with low volume , that's very bearish .
If you have a small price change with low volume , that's bearish .
Look at the chart and you'll see how trends start and end with a PINCH and widen in the middle of the moves.
You can set the moving average period, 14 is the default.
Good trading!
Brian Hershey
v2 change log...
- issue with price gaps - gaps at the open were sometimes showing incorrect colors
- scaling issues - sometimes a change is so large it scales down all nearby data and renders it hard to view. Code was added to clip those huge values.
v3 what's coming next...
- better scaling - sometimes with thinly traded stocks there is too much clipping. For now increase the chart interval to correct.
True Gap Finder with Revisit DetectionTrue Gap Finder with Revisit Detection
This indicator is a powerful tool for intraday traders to identify and track price gaps. Unlike simple gap indicators, this script actively tracks the status of the gap, visualizing the void until it is filled (revisited) by price.
Key Features:
Active Gap Tracking: Finds gap-up and gap-down occurrences (where Low > Previous High or High < Previous Low) and actively tracks them.
Gap Zones (Clouds): Visually shades the empty "gap zone" (the void between the gap candles), making it instantly obvious where price needs to travel to fill the gap. The cloud disappears automatically once the gap is filled.
Dynamic Labels: automatically displays price labels at the origin of the gap, showing the specific price range (High-Low) that constitutes the gap. Labels are positioned intelligently to avoid cluttering current price action.
Alerts: Configurable alerts notify you the moment a gap is filled.
Customization: Full control over colors, clouds, labels, and alert settings to match your chart style.
How it works: The indicator tracks the most recent gap. If a new gap forms, it becomes the active focus. When price moves back to "close" or "fill" this gap area, the lines and clouds automatically stop plotting, giving you a clean chart that focuses only on open business.
Probability Cone█ Overview:
Probability Cone is based on the Expected Move . While Expected Move only shows the historical value band on every bar, probability panel extend the period in the future and plot a cone or curve shape of the probable range. It plots the range from bar 1 all the way to bar 31.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption; it's not the real distribution of return.
The area of probability range is based on an inverse normal cumulative distribution function. The inverse cumulative distribution gives the range of price for given input probability. People can adjust the range by adjusting the standard deviation in the settings. The probability of the entered standard deviation will be shown at the edges of the probability cone.
The shown 68% and 95% probabilities correspond to the full range between the two blue lines of the cone (68%) and the two purple lines of the cone (95%). The probabilities suggest the % of outcomes or data that are expected to lie within this range. It does not suggest the probability of reaching those price levels.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
█ Volatility Models :
Sample SD : traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson : Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass : Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension : Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers : Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient.
EWMA : Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang : Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation : It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
You can learn more about each of the volatility models in out Historical Volatility Estimators indicator.
█ How to use
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended.
The shape of the cone will be skewed and have a directional bias when the length of mean is short. It might be more adaptive to the current price or trend, but more accurate estimation should use a longer period for the mean.
Using a short look back for mean will make the cone having a directional bias.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
Time back settings shift the estimation period back by the input number. It's the origin of when the probability cone start to estimation it's range.
E.g., When time back = 5, the probability cone start its prediction interval estimation from 5 bars ago. So for time back = 5 , it estimates the probability range from 5 bars ago to X number of bars in the future, specified by the Forecast Period (max 1000).
█ Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
The uncertainty in future bars makes the range wider. The overestimate effect of the body is partly neutralized when it's extended to future bars. We encourage people who use this indicator to further investigate the Historical Volatility Estimators , Fast Autocorrelation Estimator , Expected Move and especially the Linear Moments Indicator .
The probability is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
AliceTears GridAliceTears Grid is a customizable Mean Reversion system designed to capitalize on market volatility during specific trading sessions. Unlike standard grid bots that place blind limit orders, this strategy establishes a daily or session-based "Baseline" and looks for price over-extensions to fade the move back to the mean.
This strategy is best suited for ranging markets (sideways accumulation) or specific forex sessions (e.g., Asian Session or NY/London overlap) where price tends to revert to the opening price.
🛠 How It Works
1. The Baseline & Grid Generation At the start of every session (or the daily open), the script records the Open price. It then projects visual grid lines above and below this price based on your Step % input.
Example: If the Open is $100 and Step is 1%, lines are drawn at $101, $102, $99, $98, etc.
2. Entry Logic: Reversal Mode This script features a "Reversal Mode" (enabled by default) to filter out "falling knives."
Standard Grid: Buys immediately when price touches the line.
AliceTears Logic: Waits for the price to breach a grid level and then close back inside towards the mean. This confirms a potential rejection of that level before entering.
3. Exit Logic
Target Profit: The primary target is the previous grid level (Mean Reversion).
Trailing Stop: If the price continues moving in your favor, a trailing stop activates to maximize the run.
Stop Loss: A manual percentage-based stop loss is available to prevent deep drawdowns in trending markets.
⚙️ Key Features
Visual Grid: Automatically draws entry levels on the chart for the current session, helping you visualize where the "math" is waiting for price.
Timezone & Session Control: Includes a custom Timezone Offset tool. You can trade specific hours (e.g., 09:30–16:00) regardless of your chart's UTC setting.
Grid Management: Independent logic for Long and Short grids with pyramiding capabilities.
Safety Filters: Options to force-close trades at the end of the session to avoid overnight gaps.
⚠️ Risk Warning
Please Read Before Using: This is a Counter-Trend / Grid Strategy.
Pros: High win rate in sideways/ranging markets.
Cons: In strong trending markets (parabolic pumps or crashes), this strategy will add to losing positions ("catch a falling knife").
Recommendation: Always use the Stop Loss and Date Filter inputs. Do not run this on highly volatile assets without strict risk management parameters.
Settings Guide
Entry Reversal Mode: Keep checked for safer entries. Uncheck for aggressive limit-order style execution.
Grid Step (%): The distance between lines. For Forex, use lower values (0.1% - 0.5%). For Crypto, use higher values (1.0% - 3.0%).
UTC Offset: Adjust this to align the Session Hours with your target market (e.g., -5 for New York).
This script is open source. Feel free to use it for educational purposes or modify it to fit your trading style.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
Market Cycle Master The Market Cycle Master (MCM) by © DarkPoolCrypto is a sophisticated trading system designed to bridge the gap between standard retail trend indicators and institutional-grade risk management. Unlike traditional indicators that simply provide entry signals based on a single timeframe, this system employs a "Confluence Engine" that requires multi-timeframe (MTF) alignment before generating a signal.
Crucially, this script integrates a live Risk Management Calculator directly into the chart overlay. This feature allows traders to stop guessing position sizes and instead execute trades based on a fixed percentage of account equity at risk, calculating the exact lot size relative to the dynamic stop-loss level.
Core Concept and Logic
This system operates on three distinct layers of logic to filter out noise and identifying high-probability trend continuations:
1. The Trend Architecture (Layer 1) At its core, the script utilizes an adaptive ATR-based SuperTrend calculation. This allows the system to adjust to market volatility dynamically. When volatility expands, the trend bands widen to prevent premature stop-outs. When volatility contracts, the bands tighten to capture early reversals.
2. Institutional Context / Multi-Timeframe Filter (Layer 2) This is the primary filter of the Pro system. The script monitors a higher timeframe (default: 4-Hour) in the background.
Bullish Context: If the Higher Timeframe (HTF) is in an uptrend, the script will only permit LONG signals on your current chart.
Bearish Context: If the HTF is in a downtrend, the script will only permit SHORT signals.
Grayscale Filters: If the current chart's trend opposes the Higher Timeframe trend (e.g., a 5-minute uptrend during a 4-hour downtrend), the candles will be painted GRAY. This indicates a low-probability "Counter-Trend" environment, and no signals will be generated.
3. Money Flow Filtering (Layer 3) To prevent buying tops or selling bottoms, the system utilizes the Money Flow Index (MFI). Long signals are filtered if volume-weighted momentum is already overbought, and Short signals are filtered if oversold.
The Risk Management HUD
The Heads-Up Display (HUD) is the distinguishing feature of this tool. It transforms the indicator from a visual aid into a trading terminal.
Trend Direction: Displays the current verified trend.
MTF Status: Shows the state of the Higher Timeframe trend.
Volatility: Displays the current ATR value.
Stop Loss: Displays the exact price level of the trend line.
Risk Calculator:
Risk ($): Shows the total dollar amount you will lose if the stop loss is hit (based on your settings).
Units: Calculates exactly how much Crypto, Stock, or FX lots to purchase to match your risk parameters.
Guide: How to Use
Configuration
Trend Architecture: Adjust the "Volatility Factor" (Default: 3.0). Higher values reduce noise but delay entries. Lower values are faster but riskier.
Institutional Context: Select the "Higher Timeframe."
If trading 1m to 15m charts: Set HTF to 4 Hours (240).
If trading 1H to 4H charts: Set HTF to Daily (1D).
Risk Calculator:
Account Size: Enter your total trading capital.
Risk Per Trade: Enter the percentage of your account you are willing to lose on a single trade (e.g., 1.0%).
Trading Strategy
The Signal: Wait for a "Sniper Long" or "Sniper Short" label. This appears only when price action, volatility, and the higher timeframe consensus all align.
The Execution: Look at the HUD under "Units." Open a position for that specific amount.
The Stop Loss: Place your hard Stop Loss at the price shown in the HUD ("Stop Loss" row). This corresponds to the trend line.
The Exit: Close the position if the candle color turns Gray (loss of momentum/consensus) or if an opposing signal appears.
Disclaimer
This script and the information provided herein are for educational and entertainment purposes only. They do not constitute financial advice, investment advice, trading advice, or any other advice. Trading in financial markets involves a high degree of risk and may result in the loss of your entire capital.
The "Risk Calculator" included in this script provides theoretical values based on mathematical formulas relative to the price data provided by TradingView. It does not account for slippage, spread, exchange fees, or liquidity gaps. Always verify calculations manually before executing live trades. Past performance of any trading system is not indicative of future results. The author assumes no responsibility for any losses incurred while using this script.
CME Bitcoin Weekend Gap (Global) @jerikooDescription:
The Problem: You are watching the wrong hours. Many traders assume CME Bitcoin futures follow standard stock market hours or open Monday morning. This is incorrect.
Stock Market: Opens Monday morning.
CME Bitcoin: Opens Sunday Evening (US Time).
If you are in Europe, this means the market actually opens at Midnight (00:00) Monday. If you are waiting for the "Monday Morning Open," you are late.
The Solution: True Gap Detection This indicator highlights the exact downtime of the CME Bitcoin Futures market to help you identify true liquidity gaps.
Why this script is different: Most gap scripts break when you change your chart's time zone (e.g., switching from UTC to New York). This script is Universal.
Hardcoded Exchange Time: It calculates logic based on "America/Chicago" (CME HQ) time, regardless of your local chart settings.
Manual Offset Fix: Some data feeds have a +/- 1 or 2-hour sync difference depending on the broker. This script includes a "Hour Shift" setting to manually align the box perfectly to your specific candles.
How to use:
Add to your chart.
Look for the Dark Green highlighted zone.
This zone represents the Weekend Gap (Friday Close to Sunday Open).
Troubleshooting: If the box starts 1-2 hours too early or too late, go to Settings and change the "Hour Shift" value (e.g., -1, +1) until it snaps perfectly to the Friday close candle.
Technical Details:
CME Close: Friday 16:00 CT
CME Open: Sunday 17:00 CT
Color: Dark Green (50% Transparency)
Step 3: Categories & Tags
Select these options in the right-hand menu of the publishing page.
Category: Trend Analysis OR Bitcoin
Tags: CME Bitcoin BTC Gap Futures Weekend
Step 4: Final Checklist Before Clicking "Publish"
Load the Code: Make sure the "Manual Fix" version of the code (the last one I gave you) is currently open in the Pine Editor.
Add to Chart: You must click "Add to Chart" so the script is visible on your screen before publishing.
Privacy: Select Public (so others can search for it) or Private (if you only want to share the link).
Visibility: Choose Open (so others can see the code) or Protected (if you want to hide the code, though Open is better for simple scripts like this).
Liquidity Void Zone Detector [PhenLabs]📊 Liquidity Void Zone Detector
Version: PineScript™v6
📌 Description
The Liquidity Void Zone Detector is a sophisticated technical indicator designed to identify and visualize areas where price moved with abnormally low volume or rapid momentum, creating "voids" in market liquidity. These zones represent areas where insufficient trading activity occurred during price movement, often acting as magnets for future price action as the market seeks to fill these gaps.
Built on PineScript v6, this indicator employs a dual-detection methodology that analyzes both volume depletion patterns and price movement intensity relative to ATR. The revolutionary 3D visualization system uses three-layer polyline rendering with adaptive transparency and vertical offsets, creating genuine depth perception where low liquidity zones visually recede and high liquidity zones protrude forward. This makes critical market structure immediately apparent without cluttering your chart.
🚀 Points of Innovation
Dual detection algorithm combining volume threshold analysis and ATR-normalized price movement sensitivity for comprehensive void identification
Three-layer 3D visualization system with progressive transparency gradients (85%, 78%, 70%) and calculated vertical offsets for authentic depth perception
Intelligent state machine logic that tracks consecutive void bars and only renders zones meeting minimum qualification requirements
Dynamic strength scoring system (0-100 scale) that combines inverted volume ratios with movement intensity for accurate void characterization
Adaptive ATR-based spacing calculation that automatically adjusts 3D layering depth to match instrument volatility
Efficient memory management system supporting up to 100 simultaneous void visualizations with automatic array-based cleanup
🔧 Core Components
Volume Analysis Engine: Calculates rolling volume averages and compares current bar volume against dynamic thresholds to detect abnormally thin trading conditions
Price Movement Analyzer: Normalizes bar range against ATR to identify rapid price movements that indicate liquidity exhaustion regardless of instrument or timeframe
Void Tracking State Machine: Maintains persistent tracking of void start bars, price boundaries, consecutive bar counts, and cumulative strength across multiple bars
3D Polyline Renderer: Generates three-layer rectangular polylines with precise timestamp-to-bar index conversion and progressive offset calculations
Strength Calculation System: Combines volume component (inverted ratio capped at 100) with movement component (ATR intensity × 30) for comprehensive void scoring
🔥 Key Features
Automatic Void Detection: Continuously scans price action for low volume conditions or rapid movements, triggering void tracking when thresholds are exceeded
Real-Time Visualization: Creates 3D rectangular zones spanning from void initiation to termination, with color-coded depth indicating liquidity type
Adjustable Sensitivity: Configure volume threshold multiplier (0.1-2.0x), price movement sensitivity (0.5-5.0x), and minimum qualifying bars (1-10) for customized detection
Dual Color Coding: Separate visual treatment for low liquidity voids (receding red) and high liquidity zones (protruding green) based on 50-point strength threshold
Optional Compact Labels: Toggle LV (Low Volume) or HV (High Volume) circular labels at void centers for quick identification without visual clutter
Lookback Period Control: Adjust analysis window from 5 to 100 bars to match your trading timeframe and market volatility characteristics
Memory-Efficient Design: Automatically manages polyline and label arrays, deleting oldest elements when user-defined maximum is reached
Data Window Integration: Plots void detection binary, current strength score, and average volume for detailed analysis in TradingView's data window
🎨 Visualization
Three-Layer Depth System: Each void is rendered as three stacked polylines with progressive transparency (85%, 78%, 70%) and calculated vertical offsets creating authentic 3D appearance
Directional Depth Perception: Low liquidity zones recede with back layer most transparent; high liquidity zones protrude with front layer most transparent for instant visual differentiation
Adaptive Offset Spacing: Vertical separation between layers calculated as ATR(14) × 0.001, ensuring consistent 3D effect across different instruments and volatility regimes
Color Customization: Fully configurable base colors for both low liquidity zones (default: red with 80 transparency) and high liquidity zones (default: green with 80 transparency)
Minimal Chart Clutter: Closed polylines with matching line and fill colors create clean rectangular zones without unnecessary borders or visual noise
Background Highlight: Subtle yellow background (96% transparency) marks bars where void conditions are actively detected in real-time
Compact Labeling: Optional tiny circular labels with 60% transparent backgrounds positioned at void center points for quick reference
📖 Usage Guidelines
Detection Settings
Lookback Period: Default: 10 | Range: 5-100 | Number of bars analyzed for volume averaging and void detection. Lower values increase sensitivity to recent changes; higher values smooth detection across longer timeframes. Adjust based on your trading timeframe: short-term traders use 5-15, swing traders use 20-50, position traders use 50-100.
Volume Threshold: Default: 1.0 | Range: 0.1-2.0 (step 0.1) | Multiplier applied to average volume. Bars with volume below (average × threshold) trigger void conditions. Lower values detect only extreme volume depletion; higher values capture more moderate low-volume situations. Start with 1.0 and decrease to 0.5-0.7 for stricter detection.
Price Movement Sensitivity: Default: 1.5 | Range: 0.5-5.0 (step 0.1) | Multiplier for ATR-normalized price movement detection. Values above this threshold indicate rapid price changes suggesting liquidity voids. Increase to 2.0-3.0 for volatile instruments; decrease to 0.8-1.2 for ranging or low-volatility conditions.
Minimum Void Bars: Default: 10 | Range: 1-10 | Minimum consecutive bars exhibiting void conditions required before visualization is created. Filters out brief anomalies and ensures only sustained voids are displayed. Use 1-3 for scalping, 5-10 for intraday trading, 10+ for swing trading to match your time horizon.
Visual Settings
Low Liquidity Color: Default: Red (80% transparent) | Base color for zones where volume depletion or rapid movement indicates thin liquidity. These zones recede visually (back layer most transparent). Choose colors that contrast with your chart theme for optimal visibility.
High Liquidity Color: Default: Green (80% transparent) | Base color for zones with relatively higher liquidity compared to void threshold. These zones protrude visually (front layer most transparent). Ensure clear differentiation from low liquidity color.
Show Void Labels: Default: True | Toggle display of compact LV/HV labels at void centers. Disable for cleaner charts when trading; enable for analysis and review to quickly identify void types across your chart.
Max Visible Voids: Default: 50 | Range: 10-100 | Maximum number of void visualizations kept on chart. Each void uses 3 polylines, so setting of 50 maintains 150 total polylines. Higher values preserve more history but may impact performance on lower-end systems.
✅ Best Use Cases
Gap Fill Trading: Identify unfilled liquidity voids that price frequently returns to, providing high-probability retest and reversal opportunities when price approaches these zones
Breakout Validation: Distinguish genuine breakouts through established liquidity from false breaks into void zones that lack sustainable volume support
Support/Resistance Confluence: Layer void detection over key horizontal levels to validate structural integrity—levels within high liquidity zones are stronger than those in voids
Trend Continuation: Monitor for new void formation in trend direction as potential continuation zones where price may accelerate due to reduced resistance
Range Trading: Identify void zones within consolidation ranges that price tends to traverse quickly, helping to avoid getting caught in rapid moves through thin areas
Entry Timing: Wait for price to reach void boundaries rather than entering mid-void, as voids tend to be traversed quickly with limited profit-taking opportunities
⚠️ Limitations
Historical Pattern Indicator: Identifies past liquidity voids but cannot predict whether price will return to fill them or when filling might occur
No Volume on Forex: Indicator uses tick volume for forex pairs, which approximates but doesn't represent true trading volume, potentially affecting detection accuracy
Lagging Confirmation: Requires minimum consecutive bars (default 10) before void is visualized, meaning detection occurs after void formation begins
Trending Market Behavior: Strong trends driven by fundamental catalysts may create voids that remain unfilled for extended periods or permanently
Timeframe Dependency: Detection sensitivity varies significantly across timeframes; settings optimized for one timeframe may not perform well on others
No Directional Bias: Indicator identifies liquidity characteristics but provides no predictive signal for price direction after void detection
Performance Considerations: Higher max visible void settings combined with small minimum void bars can generate numerous visualizations impacting chart rendering speed
💡 What Makes This Unique
Industry-First 3D Visualization: Unlike flat volume or liquidity indicators, the three-layer rendering with directional depth perception provides instant visual hierarchy of liquidity quality
Dual-Mode Detection: Combines both volume-based and movement-based detection methodologies, capturing voids that single-approach indicators miss
Intelligent Qualification System: State machine logic prevents premature visualization by requiring sustained void conditions, reducing false signals and chart clutter
ATR-Normalized Analysis: All detection thresholds adapt to instrument volatility, ensuring consistent performance across stocks, forex, crypto, and futures without constant recalibration
Transparency-Based Depth: Uses progressive transparency gradients rather than colors or patterns to create depth, maintaining visual clarity while conveying information hierarchy
Comprehensive Strength Metrics: 0-100 void strength calculation considers both the degree of volume depletion and the magnitude of price movement for nuanced zone characterization
🔬 How It Works
Phase 1: Real-Time Detection
On each bar close, the indicator calculates average volume over the lookback period and compares current bar volume against the volume threshold multiplier
Simultaneously measures current bar's high-low range and normalizes it against ATR, comparing the result to price movement sensitivity parameter
If either volume falls below threshold OR movement exceeds sensitivity threshold, the bar is flagged as exhibiting void characteristics
Phase 2: Void Tracking & Qualification
When void conditions first appear, state machine initializes tracking variables: start bar index, initial top/bottom prices, consecutive bar counter, and cumulative strength accumulator
Each subsequent bar with void conditions extends the tracking, updating price boundaries to envelope all bars and accumulating strength scores
When void conditions cease, system checks if consecutive bar count meets minimum threshold; if yes, proceeds to visualization; if no, discards the tracking and resets
Phase 3: 3D Visualization Construction
Calculates average void strength by dividing cumulative strength by number of bars, then determines if void is low liquidity (>50 strength) or high liquidity (≤50 strength)
Generates three polyline layers spanning from start bar to end bar and from top price to bottom price, each with calculated vertical offset based on ATR
Applies progressive transparency (85%, 78%, 70%) with layer ordering creating recession effect for low liquidity zones and protrusion effect for high liquidity zones
Creates optional center label and pushes all visual elements into arrays for memory management
Phase 4: Memory Management & Display
Continuously monitors polyline array size (each void creates 3 polylines); when total exceeds max visible voids × 3, deletes oldest polylines via array.shift()
Similarly manages label array, removing oldest labels when count exceeds maximum to prevent memory accumulation over extended chart history
Plots diagnostic data to TradingView’s data window (void detection binary, current strength, average volume) for detailed analysis without cluttering main chart
💡 Note:
This indicator is designed to enhance your market structure analysis by revealing liquidity characteristics that aren’t visible through standard price and volume displays. For best results, combine void detection with your existing support/resistance analysis, trend identification, and risk management framework. Liquidity voids are descriptive of past market behavior and should inform positioning decisions rather than serve as standalone entry/exit signals. Experiment with detection parameters across different timeframes to find settings that align with your trading style and instrument characteristics.
Nadaraya-Watson: Rational Quadratic Kernel (Opening Gap Shift)What we did to fix it: We didn't throw out the old data (that made it too jumpy early in the day).
Instead, we "tricked" the kernel by shifting all the previous day's prices up or down by the exact gap amount (e.g., if it gapped up 50 points, add 50 to every old price point). This makes the history "line up" with the new day's starting level.
Created so with a fresh session the Nadaraya-Watson Regression Kernel is relevant from the get go - no catch up on opening gaps.
All credit to jdehorty his full description is below.
What is Nadaraya–Watson Regression?
Nadaraya–Watson Regression is a type of Kernel Regression, which is a non-parametric method for estimating the curve of best fit for a dataset. Unlike Linear Regression or Polynomial Regression, Kernel Regression does not assume any underlying distribution of the data. For estimation, it uses a kernel function, which is a weighting function that assigns a weight to each data point based on how close it is to the current point. The computed weights are then used to calculate the weighted average of the data points.
How is this different from using a Moving Average?
A Simple Moving Average is actually a special type of Kernel Regression that uses a Uniform (Retangular) Kernel function. This means that all data points in the specified lookback window are weighted equally. In contrast, the Rational Quadratic Kernel function used in this indicator assigns a higher weight to data points that are closer to the current point. This means that the indicator will react more quickly to changes in the data.
Why use the Rational Quadratic Kernel over the Gaussian Kernel?
The Gaussian Kernel is one of the most commonly used Kernel functions and is used extensively in many Machine Learning algorithms due to its general applicability across a wide variety of datasets. The Rational Quadratic Kernel can be thought of as a Gaussian Kernel on steroids; it is equivalent to adding together many Gaussian Kernels of differing length scales. This allows the user even more freedom to tune the indicator to their specific needs.
The formula for the Rational Quadratic function is:
K(x, x') = (1 + ||x - x'||^2 / (2 * alpha * h^2))^(-alpha)
where x and x' data are points, alpha is a hyperparameter that controls the smoothness (i.e. overall "wiggle") of the curve, and h is the band length of the kernel.
Does this Indicator Repaint?
No, this indicator has been intentionally designed to NOT repaint. This means that once a bar has closed, the indicator will never change the values in its plot. This is useful for backtesting and for trading strategies that require a non-repainting indicator.
Settings:
Bandwidth. This is the number of bars that the indicator will use as a lookback window.
Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel.
Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes.
Holographic Market Microstructure | AlphaNattHolographic Market Microstructure | AlphaNatt
A multidimensional, holographically-rendered framework designed to expose the invisible forces shaping every candle — liquidity voids, smart money footprints, order flow imbalances, and structural evolution — in real time.
---
📘 Overview
The Holographic Market Microstructure (HMS) is not a traditional indicator. It’s a visual architecture built to interpret the true anatomy of the market — a living data structure that fuses price, volume, and liquidity into one coherent holographic layer.
Instead of reacting to candles, HMS visualizes the market’s underlying micro-dynamics : where liquidity hides, where volume flows, and how structure morphs as smart money accumulates or distributes.
Designed for system-based traders, volume analysts, and liquidity theorists who demand to see the unseen — the invisible grid driving every price movement.
---
🔬 Core Analytical Modules
Microstructure Analysis
Deconstructs each bar’s internal composition to identify imbalance between aggressive buying and selling. Using a configurable Imbalance Ratio and Liquidity Threshold , the algorithm marks low-liquidity zones and price inefficiencies as “liquidity voids.”
• Detects hidden supply/demand gaps.
• Quantifies micro-level absorption and exhaustion.
• Reveals flow compression and expansion phases.
Smart Money Tracking
Applies advanced volume-rate-of-change and price momentum relationships to map institutional activity.
• Accumulation Zones – Where price rises on expanding volume.
• Distribution Zones – Where price declines on rising volume.
• Automatically visualized as glowing boxes, layered through time to simulate footprint persistence.
Fractal Structure Mapping
Reveals the recursive nature of price formation. HMS detects fractal highs/lows, then connects them into an evolving structure.
• Defines nested market structure across multiple scales.
• Maps trend progression and transition points.
• Renders with adaptive glow lines to reflect depth and strength.
Volume Heat Map
Transforms historical volume data into a 3D holographic heat projection.
• Each band represents a volume-weighted price level.
• Gradient brightness = relative participation intensity.
• Helps identify volume nodes, voids, and liquidity corridors.
HUD Display System
Real-time analytical dashboard summarizing the system’s internal metrics directly on the chart.
• Flow, Structure, Smart$, Liquidity, and Divergence — all live.
• Designed for both scalpers and swing traders to assess micro-context instantly.
---
🧠 Smart Money Intelligence Layer
The Smart Money Index dynamically evaluates the harmony (or conflict) between price momentum and volume acceleration. When institutions accumulate or distribute discreetly, volume surges ahead of price. HMS detects this divergence and overlays it as glowing smart money zones.
◈ ACCUM → Institutional absorption, early uptrend formation.
◈ DISTRIB → Distribution and top-heavy conditions.
○ IDLE → Neutral flow equilibrium.
Divergences between price and volume are signaled using holographic alerts ( ⚠ ALERT ) to highlight exhaustion or trap conditions — often precursors to structural reversals.
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🌀 Fractal Market Structure Engine
The fractal subsystem recursively identifies local pivot symmetry, connecting micro-structural highs and lows into a holographic skeleton.
• Bullish Structure — Higher highs & higher lows align (▲ BULLISH).
• Bearish Structure — Lower highs & lower lows dominate (▼ BEARISH).
• Ranging — Fractal symmetry balance (◆ RANGING).
Each transition is visually represented through adaptive glow intensity, producing a living contour of market evolution .
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🔥 Volume Heat Map Projection
The heatmap acts as a volumetric X-ray of the recent 100–300 bars. Each horizontal segment reflects liquidity density, rendered with gradient opacity from cold (inactive) to hot (highly active).
• Detects hidden accumulation shelves and distribution ridges.
• Identifies imbalanced liquidity corridors (voids).
• Reveals the invisible scaffolding of the order book.
When combined with smart money zones and structure lines, it creates a multi-layered holographic perspective — allowing traders to see liquidity clusters and their interaction with evolving structure in real time.
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💎 Holographic Visual Engine
Every element of HMS is dynamically color-mapped to its visual theme . Each theme carries a distinct personality:
Aeon — Neon blue plasma aesthetic; futuristic and fluid.
Cyber — High-contrast digital energy; circuit-like clarity.
Quantum — Deep space gradients; reflective of non-linear flow.
Neural — Organic transitions; biological intelligence simulation.
Plasma — Vapor-bright gradients; high-energy reactive feedback.
Crystal — Minimalist, transparent geometry; pristine data visibility.
Optional Glow Effects and Pulse Animations create a living hologram that responds to real-time market conditions.
---
🧭 HUD Analytics Table
A live data matrix placed anywhere on-screen (top, middle, or side). It summarizes five critical systems:
Flow: Order flow bias — ▲ BUYING / ▼ SELLING / ◆ NEUTRAL.
Struct: Microstructure direction — ▲ BULLISH / ▼ BEARISH / ◆ RANGING.
Smart$: Institutional behavior — ◈ ACCUM / ◈ DISTRIB / ○ IDLE.
Liquid: Market efficiency — ⚡ VOID / ● NORMAL.
Diverg: Price/Volume correlation — ⚠ ALERT / ✓ CLEAR.
Each metric’s color dynamically adjusts according to live readings, effectively serving as a neural HUD layer for rapid interpretation.
---
🚨 Alert Conditions
Stay informed in real time with built-in alerts that trigger under specific structural or liquidity conditions.
Liquidity Void Detected — Market inefficiency or thin volume region identified.
Strong Order Flow Detected — Aggressive buying or selling momentum shift.
Smart Money Activity — Institutional accumulation or distribution underway.
Price/Volume Divergence — Volume fails to confirm price trend.
Market Structure Shift — Fractal structure flips directional bias.
---
⚙️ Customization Parameters
Adjustable Microstructure Depth (20–200 bars).
Configurable Imbalance Ratio and Liquidity Threshold .
Adaptive Smart Money Sensitivity via Accumulation Threshold (%).
Multiple Fractal Depth Layers for precise structural analysis.
Scalable Heatmap Resolution (5–20 levels) and opacity control.
Selectable HUD Position to suit personal layout preferences.
Each parameter adjusts the balance between visual clarity and data density , ensuring optimal performance across intraday and macro timeframes alike.
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🧩 Trading Application
Identify early signs of institutional activity before breakouts.
Track structure transitions with fractal precision.
Locate hidden liquidity voids and high-value areas.
Confirm strength of trends using order-flow bias.
Detect volume-based divergences that often precede reversals.
HMS is designed not just for observation — but for contextual understanding . Its purpose is to help traders anchor strategies in liquidity and flow dynamics rather than surface-level price action.
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🪞 Philosophy
Markets are holographic. Each candle contains a reflection of every other candle — a fractal within a fractal, a structure within a structure. The HMS is built to reveal that reflection, allowing traders to see through the market’s multidimensional fabric.
---
Developed by: AlphaNatt
Version: v6
Category: Market Microstructure | Volume Intelligence
Framework: PineScript v6 | Holographic Visualization System
Not financial advice
SevenDayHighLowTableWithBoxes [CHE]SevenDayHighLowTableWithBoxes — Seven-day day-range boxes with a weekday-aware “ghost” projection and a compact table that tracks recent extremes and per-weekday hit rates.
Summary
This indicator visualizes each trading day as a colored box and annotates the final high and low with compact markers. It maintains a rolling seven-day view and a five-column table showing day name, high, low, range, and a per-weekday projection hit statistic. A dashed “ghost” box projects a typical range for the current weekday using a running average and an adjustable scaling factor. The script is written in Pine v6, runs on the main chart (overlay true), and emphasizes stable object handling and closed-bar finalization at day boundaries.
Motivation: Why this design?
Intraday traders often need fast context for where today’s price sits relative to recent daily extremes, without switching timeframes. A simple daily high/low overlay is informative but lacks structure, sizing context, and continuity. By grouping bars into local days (configurable UTC offset), drawing explicit boxes, and projecting a weekday-typical range, the chart becomes easier to scan. The compact table gives a quick audit trail of the latest seven days while tracking how often the weekday projection would have covered the realized range.
What’s different vs. standard approaches?
Reference baseline: Plain daily high/low lines or session boxes without context.
Architecture differences:
Weekday-tinted boxes and labels for today plus up to six prior days.
Weekday average range drives a dashed projection (“ghost”) sized by a user-defined percentage.
Per-weekday hit statistics recorded as hits over totals and displayed in the table.
ATR-based vertical offsets keep labels readable.
Live updates intraday; state is finalized at the local day switch.
Practical effect: The chart shows where current price sits inside a known daily envelope, plus how “typical” the day’s movement is for this weekday, aiding expectations and planning.
How it works (technical)
The script computes a local daily timestamp using the user’s UTC offset. A day change finalizes the prior day, writes its high, low, start and end indices, and records the bar indices of the terminal high and low.
For each weekday, it maintains a running average of realized ranges with a cap on the lookback count. The ghost projection length is the weekday average scaled by the user’s percentage setting.
Anchor selection for the ghost uses the most recent extreme and the close relative to the intraday midpoint to choose a low-anchored or high-anchored box.
A five-column table (Day, High, Low, Range, Ghost OK) is refreshed on the last bar. The “Ghost OK” column shows per-weekday cumulative hits over totals with a percentage, calculated before including the just-finished day.
Object counts are bounded to seven days by pruning arrays and deleting old boxes and labels. Visual updates for historical objects occur on the last bar to minimize overhead. No `security()` calls are used.
Parameter Guide
UTC (+/−) — Controls local day boundaries — Default: minus five hours — Set to your venue’s local time.
Session (for Time gate) — Session string — Default: full week — (Optional) computed internally; not applied to gating.
Show 7-Day High/Low Table — Toggles the table — Default: true — Disable to reduce UI load.
Show Day Boxes in Chart — Toggles day boxes — Default: true — Disable for a cleaner chart.
Table Position — Nine-point anchor — Default: Middle Right — Move to avoid overlap.
Table Background / Text Color / Min Cell Width — Styling controls — Defaults: gray background, white text, width twelve characters.
Weekday Colors (Sun…Sat) — Row and box tints — Defaults: semi-transparent hues — Adjust for your theme.
Triangle Transparency — Marker opacity — Default: zero — Increase to fade high/low dots.
Day Label Transparency — Day name opacity — Default: zero — Increase to reduce emphasis.
Box Border Width — Box stroke width — Default: one — Increase for stronger edges.
Extend Boxes Right — Extend current box — Default: false — Useful for forward planning.
Show Average Range Ghost Box — Dashed projection — Default: true — Disable if distracting.
Ghost Border Color / Width — Ghost styling — Defaults: gray, width one.
Ghost Length percent of AvgRange — Projection scale — Default: one hundred; bounds zero to five hundred — Lower to be conservative.
Max History Days for Average — Cap per-weekday averaging — Default: two hundred fifty-two; bounds thirty to five hundred.
ATR Length / Day Label ATR Multiplier / Triangle Up ATR Multiplier / Triangle Down ATR Multiplier — Offsets for label placement — Defaults: length one hundred; multipliers zero — Increase on dense instruments to prevent overlap.
Reading & Interpretation
Day boxes: The filled rectangle marks each day’s full high-low span; color encodes the weekday.
Markers: Small dots near the terminal high and low highlight where the final extremes occurred.
Ghost box: A dashed box sized by the weekday average range, anchored based on recent behavior. It is a typical span, not a target.
Table: Row one shows “Today”. Rows below list up to six prior days. “Ghost OK” shows per-weekday cumulative hits over totals with a percentage, which reflects historical coverage quality for that weekday.
Practical Workflows & Combinations
Trend following: Use the current box plus recent boxes to read expansion or compression days; combine with basic structure such as higher-highs and higher-lows or lower-lows and lower-highs for confirmation.
Exits and risk: When price nears the ghost boundary late in the session, consider managing exposure more conservatively.
Multi-asset and multi-timeframe: Works on minute charts. As a starting point, use five to less than sixty minutes. For cross-checks, pair with a higher timeframe bias filter.
Behavior, Constraints & Performance
Repaint/confirmation: The indicator updates intraday; extremes and ghost position can move while the day is open. Values are finalized on the next local day start.
HTF/security: None used; repaint risk is limited to live-bar movement.
Resources: `max_bars_back` five thousand; arrays are pruned to seven days; the table and color sync run on the last bar; the live ghost updates only in real time.
Known limits: Weekday averages can be unrepresentative during regime shifts, events, or gaps. Day boundaries depend on the UTC offset being set correctly. No alerts are included. The script displays warning labels when the timeframe is below five minutes or at sixty minutes and above.
Sensible Defaults & Quick Tuning
Start with the defaults.
Ghost too aggressive: Lower the percent scale.
Labels overlap: Increase ATR multipliers.
Clutter or performance issues: Hide the table or boxes, or disable the ghost.
Day boundary misaligned: Adjust the UTC offset to your market.
What this indicator is—and isn’t
This is a visualization and context layer for daily extremes and a weekday-based typical span. It does not predict direction, does not manage orders, and is not a complete trading system. Use it alongside market structure, risk controls, and position management.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
LibTmFrLibrary "LibTmFr"
This is a utility library for handling timeframes and
multi-timeframe (MTF) analysis in Pine Script. It provides a
collection of functions designed to handle common tasks related
to period detection, session alignment, timeframe construction,
and time calculations, forming a foundation for
MTF indicators.
Key Capabilities:
1. **MTF Period Engine:** The library includes functions for
managing higher-timeframe (HTF) periods.
- **Period Detection (`isNewPeriod`):** Detects the first bar
of a given timeframe. It includes custom logic to handle
multi-month and multi-year intervals where
`timeframe.change()` may not be sufficient.
- **Bar Counting (`sinceNewPeriod`):** Counts the number of
bars that have passed in the current HTF period or
returns the final count for a completed historical period.
2. **Automatic Timeframe Selection:** Offers functions for building
a top-down analysis framework:
- **Automatic HTF (`autoHTF`):** Suggests a higher timeframe
(HTF) for broader context based on the current timeframe.
- **Automatic LTF (`autoLTF`):** Suggests an appropriate lower
timeframe (LTF) for granular intra-bar analysis.
3. **Timeframe Manipulation and Comparison:** Includes tools for
working with timeframe strings:
- **Build & Split (`buildTF`, `splitTF`):** Functions to
programmatically construct valid Pine Script timeframe
strings (e.g., "4H") and parse them back into their
numeric and unit components.
- **Comparison (`isHigherTF`, `isActiveTF`, `isLowerTF`):**
A set of functions to check if a given timeframe is
higher, lower, or the same as the script's active timeframe.
- **Multiple Validation (`isMultipleTF`):** Checks if a
higher timeframe is a practical multiple of the current
timeframe. This is based on the assumption that checking
if recent, completed HTF periods contained more than one
bar is a valid proxy for preventing data gaps.
4. **Timestamp Interpolation:** Contains an `interpTimestamp()`
function that calculates an absolute timestamp by
interpolating at a given percentage across a specified
range of bars (e.g., 50% of the way through the last
20 bars), enabling time calculations at a resolution
finer than the chart's native bars.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
buildTF(quantity, unit)
Builds a Pine Script timeframe string from a numeric quantity and a unit enum.
The resulting string can be used with `request.security()` or `input.timeframe`.
Parameters:
quantity (int) : series int Number to specifie how many `unit` the timeframe spans.
unit (series TFUnit) : series TFUnit The size category for the bars.
Returns: series string A Pine-style timeframe identifier, e.g.
"5S" → 5-seconds bars
"30" → 30-minute bars
"120" → 2-hour bars
"1D" → daily bars
"3M" → 3-month bars
"24M" → 2-year bars
splitTF(tf)
Splits a Pine‑timeframe identifier into numeric quantity and unit (TFUnit).
Parameters:
tf (string) : series string Timeframe string, e.g.
"5S", "30", "120", "1D", "3M", "24M".
Returns:
quantity series int The numeric value of the timeframe (e.g., 15 for "15", 3 for "3M").
unit series TFUnit The unit of the timeframe (e.g., TFUnit.minutes, TFUnit.months).
Notes on strings without a suffix:
• Pure digits are minutes; if divisible by 60, they are treated as hours.
• An "M" suffix is months; if divisible by 12, it is converted to years.
autoHTF(tf)
Picks an appropriate **higher timeframe (HTF)** relative to the selected timeframe.
It steps up along a coarse ladder to produce sensible jumps for top‑down analysis.
Mapping → chosen HTF:
≤ 1 min → 60 (1h) ≈ ×60
≤ 3 min → 180 (3h) ≈ ×60
≤ 5 min → 240 (4h) ≈ ×48
≤ 15 min → D (1 day) ≈ ×26–×32 (regular session 6.5–8 h)
> 15 min → W (1 week) ≈ ×64–×80 for 30m; varies with input
≤ 1 h → W (1 week) ≈ ×32–×40
≤ 4 h → M (1 month) ≈ ×36–×44 (~22 trading days / month)
> 4 h → 3M (3 months) ≈ ×36–×66 (e.g., 12h→×36–×44; 8h→×53–×66)
≤ 1 day → 3M (3 months) ≈ ×60–×66 (~20–22 trading days / month)
> 1 day → 12M (1 year) ≈ ×(252–264)/quantity
≤ 1 week → 12M (1 year) ≈ ×52
> 1 week → 48M (4 years) ≈ ×(208)/quantity
= 1 M → 48M (4 years) ≈ ×48
> 1 M → error ("HTF too big")
any → error ("HTF too big")
Notes:
• Inputs in months or years are restricted: only 1M is allowed; larger months/any years throw.
• Returns a Pine timeframe string usable in `request.security()` and `input.timeframe`.
Parameters:
tf (string) : series string Selected timeframe (e.g., "D", "240", or `timeframe.period`).
Returns: series string Suggested higher timeframe.
autoLTF(tf)
Selects an appropriate **lower timeframe LTF)** for intra‑bar evaluation
based on the selected timeframe. The goal is to keep intra‑bar
loops performant while providing enough granularity.
Mapping → chosen LTF:
≤ 1 min → 1S ≈ ×60
≤ 5 min → 5S ≈ ×60
≤ 15 min → 15S ≈ ×60
≤ 30 min → 30S ≈ ×60
> 30 min → 60S (1m) ≈ ×31–×59 (for 31–59 minute charts)
≤ 1 h → 1 (1m) ≈ ×60
≤ 2 h → 2 (2m) ≈ ×60
≤ 4 h → 5 (5m) ≈ ×48
> 4 h → 15 (15m) ≈ ×24–×48 (e.g., 6h→×24, 8h→×32, 12h→×48)
≤ 1 day → 15 (15m) ≈ ×26–×32 (regular sessions ~6.5–8h)
> 1 day → 60 (60m) ≈ ×(26–32) per day × quantity
≤ 1 week → 60 (60m) ≈ ×32–×40 (≈5 sessions of ~6.5–8h)
> 1 week → 240 (4h) ≈ ×(8–10) per week × quantity
≤ 1 M → 240 (4h) ≈ ×33–×44 (~20–22 sessions × 6.5–8h / 4h)
≤ 3 M → D (1d) ≈ ×(20–22) per month × quantity
> 3 M → W (1w) ≈ ×(4–5) per month × quantity
≤ 1 Y → W (1w) ≈ ×52
> 1 Y → M (1M) ≈ ×12 per year × quantity
Notes:
• Ratios for D/W/M are given as ranges because they depend on
**regular session length** (typically ~6.5–8h, not 24h).
• Returned strings can be used with `request.security()` and `input.timeframe`.
Parameters:
tf (string) : series string Selected timeframe (e.g., "D", "240", or timeframe.period).
Returns: series string Suggested lower TF to use for intra‑bar work.
isNewPeriod(tf, offset)
Returns `true` when a new session-aligned period begins, or on the Nth bar of that period.
Parameters:
tf (string) : series string Target higher timeframe (e.g., "D", "W", "M").
offset (simple int) : simple int 0 → checks for the first bar of the new period.
1+ → checks for the N-th bar of the period.
Returns: series bool `true` if the condition is met.
sinceNewPeriod(tf, offset)
Counts how many bars have passed within a higher timeframe (HTF) period.
For daily, weekly, and monthly resolutions, the period is aligned with the trading session.
Parameters:
tf (string) : series string Target parent timeframe (e.g., "60", "D").
offset (simple int) : simple int 0 → Running count for the current period.
1+ → Finalized count for the Nth most recent *completed* period.
Returns: series int Number of bars.
isHigherTF(tf, main)
Returns `true` when the selected timeframe represents a
higher resolution than the active timeframe.
Parameters:
tf (string) : series string Selected timeframe.
main (bool) : series bool When `true`, the comparison is made against the chart's main timeframe
instead of the script's active timeframe. Optional. Defaults to `false`.
Returns: series bool `true` if `tf` > active TF; otherwise `false`.
isActiveTF(tf, main)
Returns `true` when the selected timeframe represents the
exact resolution of the active timeframe.
Parameters:
tf (string) : series string Selected timeframe.
main (bool) : series bool When `true`, the comparison is made against the chart's main timeframe
instead of the script's active timeframe. Optional. Defaults to `false`.
Returns: series bool `true` if `tf` == active TF; otherwise `false`.
isLowerTF(tf, main)
Returns `true` when the selected timeframe represents a
lower resolution than the active timeframe.
Parameters:
tf (string) : series string Selected timeframe.
main (bool) : series bool When `true`, the comparison is made against the chart's main timeframe
instead of the script's active timeframe. Optional. Defaults to `false`.
Returns: series bool `true` if `tf` < active TF; otherwise `false`.
isMultipleTF(tf)
Returns `true` if the selected timeframe (`tf`) is a practical multiple
of the active skript's timeframe. It verifies this by checking if `tf` is a higher timeframe
that has consistently contained more than one bar of the skript's timeframe in recent periods.
The period detection is session-aware.
Parameters:
tf (string) : series string The higher timeframe to check.
Returns: series bool `true` if `tf` is a practical multiple; otherwise `false`.
interpTimestamp(offStart, offEnd, pct)
Calculates a precise absolute timestamp by interpolating within a bar range based on a percentage.
This version works with RELATIVE bar offsets from the current bar.
Parameters:
offStart (int) : series int The relative offset of the starting bar (e.g., 10 for 10 bars ago).
offEnd (int) : series int The relative offset of the ending bar (e.g., 1 for 1 bar ago). Must be <= offStart.
pct (float) : series float The percentage of the bar range to measure (e.g., 50.5 for 50.5%).
Values are clamped to the range.
Returns: series int The calculated, interpolated absolute Unix timestamp in milliseconds.
Trend RiderTrend Rider is an all-in-one trading tool that helps you catch reversals, confirm trends, and spot key market levels with precision. It blends EMA clouds, volume filters, Bollinger Bands, swing levels, and session ranges into one streamlined system.
What makes Trend Rider powerful
• Dual EMA Clouds – clearly show short-term vs. long-term trend direction.
• Buy/Sell Signals – triggered on EMA crossovers, confirmed by volume strength.
• BB Reversal Mode – filters trades with Bollinger volatility and proximity to band extremes.
• Swing Levels – auto-plot important Highs/Lows as dynamic support and resistance.
• Session Ranges – highlight U.S. session and weekend boxes to track liquidity and gaps.
• Timeframe Guard – optimized exclusively for the 15-minute chart for higher accuracy.
• Alerts – every signal can fire TradingView notifications on bar close for higher reliability.
Core Value
Instead of stacking multiple tools, Trend Rider merges everything into one: trend confirmation, volume analysis, volatility filters, and key levels. The result is cleaner charts, sharper signals, and faster decisions.
Сreated with vibecoding using ChatGPT and Claude.
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
NY 14:30 High/Low - 1mThis indicator automatically draws horizontal lines for the High (green) and Low (red) of the 14:30 (Lisbon) candle on the 1-minute chart.
It is designed for traders who want to quickly identify the New York open levels (NY Open), allowing you to:
Visualize the NY market opening zone.
Use these levels as intraday support or resistance.
Plan entries and exits based on breakouts or pullbacks.
Features:
Works on any 1-minute chart.
Lines are drawn immediately after the 14:30 candle closes.
Lines extend automatically to the right.
Simple and lightweight, no complex variables or external dependencies.
Daily reset, always showing the current day’s levels.
Recommended Use:
Combine with support/resistance zones, order blocks, or fair value gaps.
Monitor price behavior during the NY open to identify breakout or rejection patterns.
Bitcoin cme gap indicators, BINANCE vs CME exchanges premium gap
# CME BTC Premium Indicator Documentation CME:BTC1!
## 1. Overview
Indicator Name: CME BTC Premium
Platform: TradingView (Pine Script v6)
Type: Premium / Gap Analysis
Purpose:
* Visualize the CME BTC futures premium/discount relative to Binance BTCUSDT spot price.
* Detect gap-up or gap-down events on the daily chart.
* Assess short-term market sentiment and potential volatility through price discrepancies.
## 2. Key Features
1. CME Premium Calculation
* Formula:
CME Premium(%) = ((CME Price - Binance Price) / Binance Price) X 100
* Positive premium: CME futures are higher than spot → Color: Blue
* Negative premium: CME futures are lower than spot → Color: Purple
2. Premium Visualization Options
* `Column` (default)
* `Line`
3. Daily Gap Detection (Daily Chart Only)
* Gap Up: CME open > previous high × 1.0001 (≥ 0.01%)
* Gap Down: CME open < previous low × 0.9999 (≤ 0.01%)
* Visualization:
* Bar Color:
* Gap Up → Yellow (semi-transparent)
* Gap Down → Blue (semi-transparent)
* Background Color:
* Gap Up → Yellow (semi-transparent)
* Gap Down → Blue (semi-transparent)
4. Label Display
* If `Show CME Label` is enabled, the last bar displays a premium percentage label.
* Label color matches premium color; text color: Black.
* Style: `style_label_upper_left`, Size: `small`.
## 3. User Inputs
| Option Name | Description | Type / Default |
| -------------- | ------------------------- | --------------------------------------- |
| Show CME Label | Display CME premium label | Boolean / true |
| CME Plot Type | CME premium chart style | String / Column (Options: Column, Line) |
## 4. Data Sources
| Data Item | Symbol | Description |
| ------------- | ---------------- | ----------------------------- |
| Binance Price | BINANCE\:BTCUSDT | Spot BTC price |
| CME Price | CME\:BTC1! | CME BTC futures closing price |
| CME Open | CME\:BTC1! | CME BTC futures open price |
| CME Low | CME\:BTC1! | CME BTC futures low price |
| CME High | CME\:BTC1! | CME BTC futures high price |
## 5. Chart Display
1. Premium Column/Line
* Displays the CME premium percentage in real-time.
* Color: Premium ≥ 0 → Blue, Premium < 0 → Purple
2. Zero Line
* Indicates CME futures are at parity with spot for quick visual reference.
3. Gap Highlight
* Applied only on daily charts.
* Gap-up or gap-down is highlighted using bar and background colors.
4. Label
* Shows the latest CME premium percentage for quick monitoring.
## 6. Use Cases
* Analyze spot-futures premium to gauge CME market sentiment.
* Identify short-term volatility and potential trend reversals through daily gaps.
* Combine premium and gap analysis to support altcoin trend analysis and position strategy.
## 7. Limitations
* This indicator does not provide investment advice or trading recommendations; it is for informational purposes only.
* Data delays, API restrictions, or exchange differences may result in calculation discrepancies.
* Gap detection is meaningful only on daily charts; other timeframes may not provide valid signals.
Normalized Open InterestNormalized Open Interest (nOI) — Indicator Overview
What it does
Normalized Open Interest (nOI) transforms raw futures open-interest data into a 0-to-100 oscillator, so you can see at a glance whether participation is unusually high or low—similar in spirit to an RSI but applied to open interest. The script positions today’s OI inside a rolling high–low range and paints it with contextual colours.
Core logic
Data source – Loads the built-in “_OI” symbol that TradingView provides for the current market.
Rolling range – Looks back a user-defined number of bars (default 500) to find the highest and lowest OI in that window.
Normalization – Calculates
nOI = (OI – lowest) / (highest – lowest) × 100
so 0 equals the minimum of the window and 100 equals the maximum.
Visual cues – Plots the oscillator plus fixed horizontal levels at 70 % and 30 % (or your own numbers). The line turns teal above the upper level, red below the lower, and neutral grey in between.
User inputs
Window Length (bars) – How many candles the indicator scans for the high–low range; larger numbers smooth the curve, smaller numbers make it more reactive.
Upper Threshold (%) – Default 70. Anything above this marks potentially crowded or overheated interest.
Lower Threshold (%) – Default 30. Anything below this marks low or capitulating interest.
Practical uses
Spot extremes – Values above the upper line can warn that the long side is crowded; values below the lower line suggest disinterest or short-side crowding.
Confirm breakouts – A price breakout backed by a sharp rise in nOI signals genuine engagement.
Look for divergences – If price makes a new high but nOI does not, participation might be fading.
Combine with volume or RSI – Layer nOI with other studies to filter false signals.
Tips
On intraday charts for non-crypto symbols the script automatically fetches daily OI data to avoid gaps.
Adjust the thresholds to 80/20 or 60/40 to fit your market and risk preferences.
Alerts, shading, or additional signal logic can be added easily because the oscillator is already normalised.
Tangent Extrapolation ForecastTangent Extrapolation Forecast
This indicator visually projects price direction by drawing a smoothed sequence of tangent lines based on recent price movements. For each bar in a user-defined lookback window, it calculates the slope over a smoothing period and extends the projected price forward. The resulting polyline forecast connect the endpoints of the extrapolations, and is color-coded to reflect directional changes: green for upward moves, red for downward, and gray for flat segments. This tool can assist traders in visualizing short-term momentum and potential trend continuity without introducing artificial future gaps.
Inputs:
Bars to Use: Number of historical bars used in the forecast.
Slope Smoothing Window: The number of bars used to calculate slope for projection.
Source: Price input for calculations (default is close).
This indicator does not generate buy/sell signals. It is intended as a visual aid to support discretionary analysis.
Ross Cameron-Inspired Day Trading StrategyExplanation for Community Members:
Title: Ross Cameron-Inspired Day Trading Strategy
Description:
This script is designed to help you identify potential buy and sell opportunities during the trading day. It combines several popular trading strategies to provide clear signals.
Key Features:
Gap and Go: Identifies stocks that have gapped up or down at the open.
Momentum Trading: Uses RSI and EMA to identify momentum-based entry points.
Mean Reversion: Uses RSI and SMA to identify potential reversals.
How to Use:
Apply to Chart: Add this script to your TradingView chart.
Set Timeframe: Works best on 5-minute and 10-minute timeframes.
Watch for Signals: Look for green "BUY" labels for entry points and red "SELL" labels for exit points.
Parameters:
Gap Percentage: Adjust to identify larger or smaller gaps.
RSI Settings: Customize the RSI length and overbought/oversold levels.
EMA and SMA Lengths: Adjust the lengths of the moving averages.
Confirmation Period: Set how many bars to wait for confirmation.
Visual Elements:
BUY Signals: Green labels below the price bars.
SELL Signals: Red labels above the price bars.
Indicators: Displays EMA (blue) and SMA (orange) for additional context.
This script is a powerful tool for day trading on NSE and BSE indices, combining multiple strategies to provide robust trading signals. Adjust the parameters to suit your trading style and always combine with your own analysis for best results.
Uptrick X PineIndicators: Z-Score Flow StrategyThis strategy is based on the Z-Score Flow Indicator developed by Uptrick. Full credit for the original concept and logic goes to Uptrick.
The Z-Score Flow Strategy combines statistical mean-reversion logic with trend filtering, RSI confirmation, and multi-mode trade execution, offering a flexible and structured approach to trading both reversals and trend continuations.
Core Concepts Behind Z-Score Flow
1. Z-Score Mean Reversion Logic
The Z-score measures how far current price deviates from its statistical mean, in standard deviations.
A high positive Z-score (e.g. > 2) suggests price is overbought and may revert downward.
A low negative Z-score (e.g. < -2) suggests price is oversold and may revert upward.
The strategy uses Z-score thresholds to trigger signals when price deviates far enough from its mean.
2. Trend Filtering with EMA
To prevent counter-trend entries, the strategy includes a trend filter based on a 50-period EMA:
Only allows long entries if price is below EMA (mean-reversion in downtrends).
Only allows short entries if price is above EMA (mean-reversion in uptrends).
3. RSI Confirmation and Lockout System
An RSI smoothing mechanism helps confirm signals and avoid whipsaws:
RSI must be below 30 and rising to allow buys.
RSI must be above 70 and falling to allow sells.
Once a signal occurs, it is "locked out" until RSI re-enters the neutral zone (30–70).
This avoids multiple signals in overextended zones and reduces overtrading.
Entry Signal Logic
A buy or sell is triggered when:
Z-score crosses below (buy) or above (sell) the threshold.
RSI smoothed condition is met (oversold and rising / overbought and falling).
The trend condition (EMA filter) aligns.
A cooldown period has passed since the last opposite trade.
This layered approach helps ensure signal quality and timing precision.
Trade Modes
The strategy includes three distinct trade modes to adapt to various market behaviors:
1. Standard Mode
Trades are opened using the Z-score + RSI + trend filter logic.
Each signal must pass all layered conditions.
2. Zero Cross Mode
Trades are based on the Z-score crossing zero.
This mode is useful in trend continuation setups, rather than mean reversion.
3. Trend Reversal Mode
Trades occur when the mean slope direction changes, i.e., basis line changes color.
Helps capture early trend shifts with less lag.
Each mode can be customized for long-only, short-only, or long & short execution.
Visual Components
1. Z-Score Mean Line
The basis (mean) line is colored based on slope direction.
Green = bullish slope, Purple = bearish slope, Gray = flat.
A wide shadow band underneath reflects current trend momentum.
2. Gradient Fill to Price
A gradient zone between price and the mean reflects:
Price above mean = bearish zone with purple overlay.
Price below mean = bullish zone with teal overlay.
This visual aid quickly reveals market positioning relative to equilibrium.
3. Signal Markers
"𝓤𝓹" labels appear for buy signals.
"𝓓𝓸𝔀𝓷" labels appear for sell signals.
These are colored and positioned according to trend context.
Customization Options
Z-Score Period & Thresholds: Define sensitivity to price deviations.
EMA Trend Filter Length: Filter entries with long-term bias.
RSI & Smoothing Periods: Fine-tune RSI confirmation conditions.
Cooldown Period: Prevent signal spam and enforce timing gaps.
Slope Index: Adjust how far back to compare mean slope.
Visual Settings: Toggle mean lines, gradients, and more.
Use Cases & Strategy Strengths
1. Mean-Reversion Trading
Ideal for catching pullbacks in trending markets or fading overextended price moves.
2. Trend Continuation or Reversal
With multiple trade modes, traders can choose between fading price extremes or trading slope momentum.
3. Signal Clarity and Risk Control
The combination of Z-score, RSI, EMA trend, and cooldown logic provides high-confidence signals with built-in filters.
Conclusion
The Z-Score Flow Strategy by Uptrick X PineIndicators is a versatile and structured trading system that:
Fuses statistical deviation (Z-score) with technical filters.
Provides both mean-reversion and trend-based entry logic.
Uses visual overlays and signal labels for clarity.
Prevents noise-driven trades via cooldown and lockout systems.
This strategy is well-suited for traders seeking a data-driven, multi-condition entry framework that can adapt to various market types.
Full credit for the original concept and indicator goes to Uptrick.
Low Liquidity Zones [PhenLabs]📊 Low Liquidity Zones
Version: PineScript™ v6
📌 Description
Low Liquidity Zones identifies and highlights periods of unusually low trading volume on your chart, marking areas where price movement occurred with minimal participation. These zones often represent potential support and resistance levels that may be more susceptible to price breakouts or reversals when revisited with higher volume.
Unlike traditional volume analysis tools that focus on high volume spikes, this indicator specializes in detecting low liquidity areas where price moved with minimal resistance. Each zone displays its volume delta, providing insight into buying vs. selling pressure during these thin liquidity periods. This combination of low volume detection and delta analysis helps traders identify potential price inefficiencies and weak structures in the market.
🚀 Points of Innovation
• Identifies low liquidity zones that most volume indicators overlook but which often become significant technical levels
• Displays volume delta within each zone, showing net buying/selling pressure during low liquidity periods
• Dynamically adjusts to different timeframes, allowing analysis across multiple time horizons
• Filters zones by maximum size percentage to focus only on precise price levels
• Maintains historical zones until they expire based on your lookback settings, creating a cumulative map of potential support/resistance areas
🔧 Core Components
• Low Volume Detection: Identifies candles where volume falls below a specified threshold relative to recent average volume, highlighting potential liquidity gaps.
• Volume Delta Analysis: Calculates and displays the net buying/selling pressure within each low liquidity zone, providing insight into the directional bias during low participation periods.
• Dynamic Timeframe Adjustment: Automatically scales analysis periods to match your selected timeframe preference, ensuring consistent identification of low liquidity zones regardless of chart settings.
• Zone Management System: Creates, tracks, and expires low liquidity zones based on your configured settings, maintaining visual clarity on the chart.
🔥 Key Features
• Low Volume Identification: Automatically detects and highlights candles where volume falls below your specified threshold compared to the moving average.
• Volume Delta Visualization: Shows the net volume delta within each zone, providing insight into whether buyers or sellers were dominant despite the low overall volume.
• Flexible Timeframe Analysis: Analyze low liquidity zones across multiple predefined timeframes or use a custom lookback period specific to your trading style.
• Zone Size Filtering: Filters out excessively large zones to focus only on precise price levels, improving signal quality.
• Automatic Zone Expiration: Older zones are automatically removed after your specified lookback period to maintain a clean, relevant chart display.
🎨 Visualization
• Volume Delta Labels: Each zone displays its volume delta with “+” or “-” prefix and K/M suffix for easy interpretation, showing the strength and direction of pressure during the low volume period.
• Persistent Historical Mapping: Zones remain visible for your specified lookback period, creating a cumulative map of potential support and resistance levels forming under low liquidity conditions.
📖 Usage Guidelines
Analysis Timeframe
Default: 1D
Range/Options: 15M, 1HR, 3HR, 4HR, 8HR, 16HR, 1D, 3D, 5D, 1W, Custom
Description: Determines the historical period to analyze for low liquidity zones. Shorter timeframes provide more recent data while longer timeframes offer a more comprehensive view of significant zones. Use Custom option with the setting below for precise control.
Custom Period (Bars)
Default: 1000
Range: 1+
Description: Number of bars to analyze when using Custom timeframe option. Higher values show more historical zones but may impact performance.
Volume Analysis
Volume Threshold Divisor
Default: 0.5
Range: 0.1-1.0
Description: Maximum volume relative to average to identify low volume zones. Example: 0.5 means volume must be below 50% of the average to qualify as low volume. Lower values create more selective zones while higher values identify more zones.
Volume MA Length
Default: 15
Range: 1+
Description: Period length for volume moving average calculation. Shorter periods make the indicator more responsive to recent volume changes, while longer periods provide a more stable baseline.
Zone Settings
Zone Fill Color
Default: #2196F3 (80% transparency)
Description: Color and transparency of the low liquidity zones. Choose colors that stand out against your chart background without obscuring price action.
Maximum Zone Size %
Default: 0.5
Range: 0.1+
Description: Maximum allowed height of a zone as percentage of price. Larger zones are filtered out. Lower values create more precise zones focusing on tight price ranges.
Display Options
Show Volume Delta
Default: true
Description: Toggles the display of volume delta within each zone. Enabling this provides additional insight into buying vs. selling pressure during low volume periods.
Delta Text Position
Default: Right
Options: Left, Center, Right
Description: Controls the horizontal alignment of the delta text within zones. Adjust based on your chart layout for optimal readability.
✅ Best Use Cases
• Identifying potential support and resistance levels that formed during periods of thin liquidity
• Spotting price inefficiencies where larger players may have moved price with minimal volume
• Finding low-volume consolidation areas that may serve as breakout or reversal zones when revisited
• Locating potential stop-hunting zones where price moved on minimal participation
• Complementing traditional support/resistance analysis with volume context
⚠️ Limitations
• Requires volume data to function; will not work on symbols where the data provider doesn’t supply volume information
• Low volume zones don’t guarantee future support/resistance - they simply highlight potential areas of interest
• Works best on liquid instruments where volume data has meaningful fluctuations
• Historical analysis is limited by the maximum allowed box count (500) in TradingView
• Volume delta in some markets may not perfectly reflect buying vs. selling pressure due to data limitations
💡 What Makes This Unique
• Focus on Low Volume: Unlike some indicators that highlight high volume events particularly like our very own TLZ indicator, this tool specifically identifies potentially significant price zones that formed with minimal participation.
• Delta + Low Volume Integration: Combines volume delta analysis with low volume detection to reveal directional bias during thin liquidity periods.
• Flexible Lookback System: The dynamic timeframe system allows analysis across any timeframe while maintaining consistent zone identification criteria.
• Support/Resistance Zone Generation: Automatically builds a visual map of potential technical levels based on volume behavior rather than just price patterns.
🔬 How It Works
1. Volume Baseline Calculation:
The indicator calculates a moving average of volume over your specified period to establish a baseline for normal market participation. This adaptive baseline accounts for natural volume fluctuations across different market conditions.
2. Low Volume Detection:
Each candle’s volume is compared to the moving average and flagged when it falls below your threshold divisor. The indicator also filters zones by maximum size to ensure only precise price levels are highlighted.
3. Volume Delta Integration:
For each identified low volume candle, the indicator retrieves the volume delta from a lower timeframe. This delta value is formatted with appropriate scaling (K/M) and displayed within the zone.
4. Zone Management:
New zones are created and tracked in a dynamic array, with each zone extending rightward until it expires. The system automatically removes expired zones based on your lookback period to maintain a clean chart.
💡 Note:
Low liquidity zones often represent areas where price moved with minimal participation, which can indicate potential market inefficiencies. These zones frequently become important support/resistance levels when revisited, especially if approached with higher volume. Consider using this indicator alongside traditional technical analysis tools for comprehensive market context. For best results, experiment with different volume threshold settings based on the specific instrument’s typical volume patterns.
