Correlation HeatMap [TradingFinder] Sessions Data Science Stats🔵 Introduction
n financial markets, correlation describes the statistical relationship between the price movements of two assets and how they interact over time. It plays a key role in both trading and investing by helping analyze asset behavior, manage portfolio risk, and understand intermarket dynamics. The Correlation Heatmap is a visual tool that shows how the correlation between multiple assets and a central reference asset (the Main Symbol) changes over time.
It supports four market types forex, stocks, crypto, and a custom mode making it adaptable to different trading environments. The heatmap uses a color-coded grid where warmer tones represent stronger negative correlations and cooler tones indicate stronger positive ones. This intuitive color system allows traders to quickly identify when assets move together or diverge, offering real-time insights that go beyond traditional correlation tables.
🟣 How to Interpret the Heatmap Visually ?
Each cell represents the correlation between the main symbol and one compared asset at a specific time.
Warm colors (e.g. red, orange) suggest strong negative correlation as one asset rises, the other tends to fall.
Cool colors (e.g. blue, green) suggest strong positive correlation both assets tend to move in the same direction.
Lighter shades indicate weaker correlations, while darker shades indicate stronger correlations.
The heatmap updates over time, allowing users to detect changes in correlation during market events or trading sessions.
One of the standout features of this indicator is its ability to overlay global market sessions such as Tokyo, London, New York, or major equity opens directly onto the heatmap timeline. This alignment lets traders observe how correlation structures respond to real-world session changes. For example, they can spot when assets shift from being inversely correlated to moving together as a new session opens, potentially signaling new momentum or macro flow. The customizable symbol setup (including up to 20 compared assets) makes it ideal not only for forex and crypto traders but also for multi-asset and sector-based stock analysis.
🟣 Use Cases and Advantages
Analyze sector rotation in equities by tracking correlation to major indices like SPX or DJI.
Monitor altcoin behavior relative to Bitcoin to find early entry opportunities in crypto markets.
Detect changes in currency alignment with DXY across trading sessions in forex.
Identify correlation breakdowns during market volatility, signaling possible new trends.
Use correlation shifts as confirmation for trade setups or to hedge multi-asset exposure
🔵 How to Use
Correlation is one of the core concepts in financial analysis and allows traders to understand how assets behave in relation to one another. The Correlation Heatmap extends this idea by going beyond a simple number or static matrix. Instead, it presents a dynamic visual map of how correlations shift over time.
In this indicator, a Main Symbol is selected as the reference point for analysis. In standard modes such as forex, stocks, or crypto, the symbol currently shown on the main chart is automatically used as the main symbol. This allows users to begin correlation analysis right away without adjusting any settings.
The horizontal axis of the heatmap shows time, while the vertical axis lists the selected assets. Each cell on the heatmap shows the correlation between that asset and the main symbol at a given moment.
This approach is especially useful for intermarket analysis. In forex, for example, tracking how currency pairs like OANDA:EURUSD EURUSD, FX:GBPUSD GBPUSD, and PEPPERSTONE:AUDUSD AUDUSD correlate with TVC:DXY DXY can give insight into broader capital flow.
If these pairs start showing increasing positive correlation with DXY say, shifting from blue to light green it could signal the start of a new phase or reversal. Conversely, if negative correlation fades gradually, it may suggest weakening relationships and more independent or volatile movement.
In the crypto market, watching how altcoins correlate with Bitcoin can help identify ideal entry points in secondary assets. In the stock market, analyzing how companies within the same sector move in relation to a major index like SP:SPX SPX or DJ:DJI DJI is also a highly effective technique for both technical and fundamental analysts.
This indicator not only visualizes correlation but also displays major market sessions. When enabled, this feature helps traders observe how correlation behavior changes at the start of each session, whether it's Tokyo, London, New York, or the opening of stock exchanges. Many key shifts, breakouts, or reversals tend to happen around these times, and the heatmap makes them easy to spot.
Another important feature is the market selection mode. Users can switch between forex, crypto, stocks, or custom markets and see correlation behavior specific to each one. In custom mode, users can manually select any combination of symbols for more advanced or personalized analysis. This makes the heatmap valuable not only for forex traders but also for stock traders, crypto analysts, and multi-asset strategists.
Finally, the heatmap's color-coded design helps users make sense of the data quickly. Warm colors such as red and orange reflect stronger negative correlations, while cool colors like blue and green represent stronger positive relationships. This simplicity and clarity make the tool accessible to both beginners and experienced traders.
🔵 Settings
Correlation Period: Allows you to set how many historical bars are used for calculating correlation. A higher number means a smoother, slower-moving heatmap, while a lower number makes it more responsive to recent changes.
Select Market: Lets you choose between Forex, Stock, Crypto, or Custom. In the first three options, the chart’s active symbol is automatically used as the Main Symbol. In Custom mode, you can manually define the Main Symbol and up to 20 Compared Symbols.
Show Open Session: Enables the display of major trading sessions such as Tokyo, London, New York, or equity market opening hours directly on the timeline. This helps you connect correlation shifts with real-world market activity.
Market Mode: Lets you select whether the displayed sessions relate to the forex or stock market.
🔵 Conclusion
The Correlation Heatmap is a robust and flexible tool for analyzing the relationship between assets across different markets. By tracking how correlations change in real time, traders can better identify alignment or divergence between symbols and gain valuable insights into market structure.
Support for multiple asset classes, session overlays, and intuitive visual cues make this one of the most effective tools for intermarket analysis.
Whether you’re looking to manage portfolio risk, validate entry points, or simply understand capital flow across markets, this heatmap provides a clear and actionable perspective that you can rely on.
Heatmap
Institutional HeatmapHeatmap Range - Volume Profile Visualization Indicator
What This Indicator Does
The Heatmap Range indicator provides a sophisticated volume profile visualization that displays price levels with the highest trading activity using color-coded heatmaps directly on your chart. Unlike traditional volume indicators, this tool shows WHERE the most significant trading occurred within specific price ranges over a customizable lookback period.
Advanced Volume Analysis
Volume-Weighted Price Levels: Calculates and displays up to 20 price levels based on actual trading volume
Customizable Time Period: Analyze volume distribution over 10-500 bars (default: 180 bars)
Smart Bin Sizing: Adjustable pip range (0.1-50 pips) for precise level identification
Peak Detection: Automatically identifies and centers display around highest volume areas
Visual Customization
3 Color Patterns:
Inverted Heat (Orange to Dark Red)
Inverted Cool (Orange to Dark Blue)
Inverted Purple (Light Pink to Dark Purple)
Transparency Control: 0-95% transparency for optimal chart readability
Adaptive Display: Shows most relevant levels centered around peak volume
Multi-Asset Support
Forex Pairs (EUR/USD, GBP/USD, etc.)
Precious Metals (Gold/XAUUSD, Silver/XAGUSD)
Futures (NQ, ES, YM, etc.)
Cryptocurrencies
Stock Indices
Customizable Parameters
Histogram Period (10-500, Default: 180) Bars to analyze for volume distribution
Bin Range (0.1-50 pips, Default: 5.0) Price range for each volume level
Color Pattern (1-3, Default: 1) Visual color scheme selection
Average Volume Period (10-200, Default: 100) Period for volume normalization
Max Display Levels (5-20, Default: 20) Maximum price levels to show
Transparency (0-95%, Default: 50%) Opacity of heatmap display
How to Use
For Day Traders
Identify key support/resistance levels based on actual volume
Spot high-probability reversal zones
Plan entries/exits around significant volume levels
For Swing Traders
Analyze longer-term volume distribution patterns
Identify major accumulation/distribution zones
Confirm breakout levels with volume validation
For Scalpers
Quick identification of intraday volume hotspots
Real-time volume level updates
Precise entry/exit timing around volume clusters
Visual Interpretation
Darker Colors: Higher volume concentration (stronger levels)
Lighter Colors: Lower volume concentration
Color Intensity: Directly correlates to volume strength at that price level
Level Positioning: Automatically centers around peak volume areas
Technical Specifications
Pine Script Version: v5
Chart Overlay: Yes
Max Bars Back: 1000
Performance Optimized: Limited to 200 bins for smooth operation
Real-time Updates: Dynamic calculation on each bar close
Getting Started
Add to Chart: Apply indicator to any supported timeframe
Adjust Period: Set histogram period based on your trading style
Choose Colors: Select color pattern that suits your chart theme
Fine-tune Levels: Adjust bin range and max levels for optimal display
Set Transparency: Balance visibility with chart clarity
Important Notes
Minimum Data Requirement: Needs at least 10 bars of history to function
Performance: Higher periods and smaller bin ranges require more processing
Volume Dependency: Most effective on instruments with consistent volume data
Timeframe Agnostic: Works on all timeframes from 1-minute to monthly
Status Information
The indicator includes a real-time information table showing:
Current settings (Period, Bin Range, Color Pattern, Transparency)
Indicator status (Active/Loading)
Disclaimer: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before making trading decisions.
Compatible with TradingView's Pine Script v5 | Optimized for all market conditions | Professional-grade volume analysis
MERV: Market Entropy & Rhythm Visualizer [BullByte]The MERV (Market Entropy & Rhythm Visualizer) indicator analyzes market conditions by measuring entropy (randomness vs. trend), tradeability (volatility/momentum), and cyclical rhythm. It provides traders with an easy-to-read dashboard and oscillator to understand when markets are structured or choppy, and when trading conditions are optimal.
Purpose of the Indicator
MERV’s goal is to help traders identify different market regimes. It quantifies how structured or random recent price action is (entropy), how strong and volatile the movement is (tradeability), and whether a repeating cycle exists. By visualizing these together, MERV highlights trending vs. choppy environments and flags when conditions are favorable for entering trades. For example, a low entropy value means prices are following a clear trend line, whereas high entropy indicates a lot of noise or sideways action. The indicator’s combination of measures is original: it fuses statistical trend-fit (entropy), volatility trends (ATR and slope), and cycle analysis to give a comprehensive view of market behavior.
Why a Trader Should Use It
Traders often need to know when a market trend is reliable vs. when it is just noise. MERV helps in several ways: it shows when the market has a strong direction (low entropy, high tradeability) and when it’s ranging (high entropy). This can prevent entering trend-following strategies during choppy periods, or help catch breakouts early. The “Optimal Regime” marker (a star) highlights moments when entropy is very low and tradeability is very high, typically the best conditions for trend trades. By using MERV, a trader gains an empirical “go/no-go” signal based on price history, rather than guessing from price alone. It’s also adaptable: you can apply it to stocks, forex, crypto, etc., on any timeframe. For example, during a bullish phase of a stock, MERV will turn green (Trending Mode) and often show a star, signaling good follow-through. If the market later grinds sideways, MERV will shift to magenta (Choppy Mode), warning you that trend-following is now risky.
Why These Components Were Chosen
Market Entropy (via R²) : This measures how well recent prices fit a straight line. We compute a linear regression on the last len_entropy bars and calculate R². Entropy = 1 - R², so entropy is low when prices follow a trend (R² near 1) and high when price action is erratic (R² near 0). This single number captures trend strength vs noise.
Tradeability (ATR + Slope) : We combine two familiar measures: the Average True Range (ATR) (normalized by price) and the absolute slope of the regression line (scaled by ATR). Together they reflect how active and directional the market is. A high ATR or strong slope means big moves, making a trend more “tradeable.” We take a simple average of the normalized ATR and slope to get tradeability_raw. Then we convert it to a percentile rank over the lookback window so it’s stable between 0 and 1.
Percentile Ranks : To make entropy and tradeability values easy to interpret, we convert each to a 0–100 rank based on the past len_entropy periods. This turns raw metrics into a consistent scale. (For example, an entropy rank of 90 means current entropy is higher than 90% of recent values.) We then divide by 100 to plot them on a 0–1 scale.
Market Mode (Regime) : Based on those ranks, MERV classifies the market:
Trending (Green) : Low entropy rank (<40%) and high tradeability rank (>60%). This means the market is structurally trending with high activity.
Choppy (Magenta) : High entropy rank (>60%) and low tradeability rank (<40%). This is a mostly random, low-momentum market.
Neutral (Cyan) : All other cases. This covers mixed regimes not strongly trending or choppy.
The mode is shown as a colored bar at the bottom: green for trending, magenta for choppy, cyan for neutral.
Optimal Regime Signal : Separately, we mark an “optimal” condition when entropy_norm < 0.3 and tradeability > 0.7 (both normalized 0–1). When this is true, a ★ star appears on the bottom line. This star is colored white when truly optimal, gold when only tradeability is high (but entropy not quite low enough), and black when neither condition holds. This gives a quick visual cue for very favorable conditions.
What Makes MERV Stand Out
Holistic View : Unlike a single-oscillator, MERV combines trend, volatility, and cycle analysis in one tool. This multi-faceted approach is unique.
Visual Dashboard : The fixed on-chart dashboard (shown at your chosen corner) summarizes all metrics in bar/gauge form. Even a non-technical user can glance at it: more “█” blocks = a higher value, colors match the plots. This is more intuitive than raw numbers.
Adaptive Thresholds : Using percentile ranks means MERV auto-adjusts to each market’s character, rather than requiring fixed thresholds.
Cycle Insight : The rhythm plot adds information rarely found in indicators – it shows if there’s a repeating cycle (and its period in bars) and how strong it is. This can hint at natural bounce or reversal intervals.
Modern Look : The neon color scheme and glow effects make the lines easy to distinguish (blue/pink for entropy, green/orange for tradeability, etc.) and the filled area between them highlights when one dominates the other.
Recommended Timeframes
MERV can be applied to any timeframe, but it will be more reliable on higher timeframes. The default len_entropy = 50 and len_rhythm = 30 mean we use 30–50 bars of history, so on a daily chart that’s ~2–3 months of data; on a 1-hour chart it’s about 2–3 days. In practice:
Swing/Position traders might prefer Daily or 4H charts, where the calculations smooth out small noise. Entropy and cycles are more meaningful on longer trends.
Day trader s could use 15m or 1H charts if they adjust the inputs (e.g. shorter windows). This provides more sensitivity to intraday cycles.
Scalpers might find MERV too “slow” unless input lengths are set very low.
In summary, the indicator works anywhere, but the defaults are tuned for capturing medium-term trends. Users can adjust len_entropy and len_rhythm to match their chart’s volatility. The dashboard position can also be moved (top-left, bottom-right, etc.) so it doesn’t cover important chart areas.
How the Scoring/Logic Works (Step-by-Step)
Compute Entropy : A linear regression line is fit to the last len_entropy closes. We compute R² (goodness of fit). Entropy = 1 – R². So a strong straight-line trend gives low entropy; a flat/noisy set of points gives high entropy.
Compute Tradeability : We get ATR over len_entropy bars, normalize it by price (so it’s a fraction of price). We also calculate the regression slope (difference between the predicted close and last close). We scale |slope| by ATR to get a dimensionless measure. We average these (ATR% and slope%) to get tradeability_raw. This represents how big and directional price moves are.
Convert to Percentiles : Each new entropy and tradeability value is inserted into a rolling array of the last 50 values. We then compute the percentile rank of the current value in that array (0–100%) using a simple loop. This tells us where the current bar stands relative to history. We then divide by 100 to plot on .
Determine Modes and Signal : Based on these normalized metrics: if entropy < 0.4 and tradeability > 0.6 (40% and 60% thresholds), we set mode = Trending (1). If entropy > 0.6 and tradeability < 0.4, mode = Choppy (-1). Otherwise mode = Neutral (0). Separately, if entropy_norm < 0.3 and tradeability > 0.7, we set an optimal flag. These conditions trigger the colored mode bars and the star line.
Rhythm Detection : Every bar, if we have enough data, we take the last len_rhythm closes and compute the mean and standard deviation. Then for lags from 5 up to len_rhythm, we calculate a normalized autocorrelation coefficient. We track the lag that gives the maximum correlation (best match). This “best lag” divided by len_rhythm is plotted (a value between 0 and 1). Its color changes with the correlation strength. We also smooth the best correlation value over 5 bars to plot as “Cycle Strength” (also 0 to 1). This shows if there is a consistent cycle length in recent price action.
Heatmap (Optional) : The background color behind the oscillator panel can change with entropy. If “Neon Rainbow” style is on, low entropy is blue and high entropy is pink (via a custom color function), otherwise a classic green-to-red gradient can be used. This visually reinforces the entropy value.
Volume Regime (Dashboard Only) : We compute vol_norm = volume / sma(volume, len_entropy). If this is above 1.5, it’s considered high volume (neon orange); below 0.7 is low (blue); otherwise normal (green). The dashboard shows this as a bar gauge and percentage. This is for context only.
Oscillator Plot – How to Read It
The main panel (oscillator) has multiple colored lines on a 0–1 vertical scale, with horizontal markers at 0.2 (Low), 0.5 (Mid), and 0.8 (High). Here’s each element:
Entropy Line (Blue→Pink) : This line (and its glow) shows normalized entropy (0 = very low, 1 = very high). It is blue/green when entropy is low (strong trend) and pink/purple when entropy is high (choppy). A value near 0.0 (below 0.2 line) indicates a very well-defined trend. A value near 1.0 (above 0.8 line) means the market is very random. Watch for it dipping near 0: that suggests a strong trend has formed.
Tradeability Line (Green→Yellow) : This represents normalized tradeability. It is colored bright green when tradeability is low, transitioning to yellow as tradeability increases. Higher values (approaching 1) mean big moves and strong slopes. Typically in a market rally or crash, this line will rise. A crossing above ~0.7 often coincides with good trend strength.
Filled Area (Orange Shade) : The orange-ish fill between the entropy and tradeability lines highlights when one dominates the other. If the area is large, the two metrics diverge; if small, they are similar. This is mostly aesthetic but can catch the eye when the lines cross over or remain close.
Rhythm (Cycle) Line : This is plotted as (best_lag / len_rhythm). It indicates the relative period of the strongest cycle. For example, a value of 0.5 means the strongest cycle was about half the window length. The line’s color (green, orange, or pink) reflects how strong that cycle is (green = strong). If no clear cycle is found, this line may be flat or near zero.
Cycle Strength Line : Plotted on the same scale, this shows the autocorrelation strength (0–1). A high value (e.g. above 0.7, shown in green) means the cycle is very pronounced. Low values (pink) mean any cycle is weak and unreliable.
Mode Bars (Bottom) : Below the main oscillator, thick colored bars appear: a green bar means Trending Mode, magenta means Choppy Mode, and cyan means Neutral. These bars all have a fixed height (–0.1) and make it very easy to see the current regime.
Optimal Regime Line (Bottom) : Just below the mode bars is a thick horizontal line at –0.18. Its color indicates regime quality: White (★) means “Optimal Regime” (very low entropy and high tradeability). Gold (★) means not quite optimal (high tradeability but entropy not low enough). Black means neither condition. This star line quickly tells you when conditions are ideal (white star) or simply good (gold star).
Horizontal Guides : The dotted lines at 0.2 (Low), 0.5 (Mid), and 0.8 (High) serve as reference lines. For example, an entropy or tradeability reading above 0.8 is “High,” and below 0.2 is “Low,” as labeled on the chart. These help you gauge values at a glance.
Dashboard (Fixed Corner Panel)
MERV also includes a compact table (dashboard) that can be positioned in any corner. It summarizes key values each bar. Here is how to read its rows:
Entropy : Shows a bar of blocks (█ and ░). More █ blocks = higher entropy. It also gives a percentage (rounded). A full bar (10 blocks) with a high % means very chaotic market. The text is colored similarly (blue-green for low, pink for high).
Rhythm : Shows the best cycle period in bars (e.g. “15 bars”). If no calculation yet, it shows “n/a.” The text color matches the rhythm line.
Cycle Strength : Gives the cycle correlation as a percentage (smoothed, as shown on chart). Higher % (green) means a strong cycle.
Tradeability : Displays a 10-block gauge for tradeability. More blocks = more tradeable market. It also shows “gauge” text colored green→yellow accordingly.
Market Mode : Simply shows “Trending”, “Choppy”, or “Neutral” (cyan text) to match the mode bar color.
Volume Regime : Similar to tradeability, shows blocks for current volume vs. average. Above-average volume gives orange blocks, below-average gives blue blocks. A % value indicates current volume relative to average. This row helps see if volume is abnormally high or low.
Optimal Status (Large Row) : In bold, either “★ Optimal Regime” (white text) if the star condition is met, “★ High Tradeability” (gold text) if tradeability alone is high, or “— Not Optimal” (gray text) otherwise. This large row catches your eye when conditions are ripe.
In short, the dashboard turns the numeric state into an easy read: filled bars, colors, and text let you see current conditions without reading the plot. For instance, five blue blocks under Entropy and “25%” tells you entropy is low (good), and a row showing “Trending” in green confirms a trend state.
Real-Life Example
Example : Consider a daily chart of a trending stock (e.g. “AAPL, 1D”). During a strong uptrend, recent prices fit a clear upward line, so Entropy would be low (blue line near bottom, perhaps below the 0.2 line). Volatility and slope are high, so Tradeability is high (green-yellow line near top). In the dashboard, Entropy might show only 1–2 blocks (e.g. 10%) and Tradeability nearly full (e.g. 90%). The Market Mode bar turns green (Trending), and you might see a white ★ on the optimal line if conditions are very good. The Volume row might light orange if volume is above average during the rally. In contrast, imagine the same stock later in a tight range: Entropy will rise (pink line up, more blocks in dashboard), Tradeability falls (fewer blocks), and the Mode bar turns magenta (Choppy). No star appears in that case.
Consolidated Use Case : Suppose on XYZ stock the dashboard reads “Entropy: █░░░░░░░░ 20%”, “Tradeability: ██████████ 80%”, Mode = Trending (green), and “★ Optimal Regime.” This tells the trader that the market is in a strong, low-noise trend, and it might be a good time to follow the trend (with appropriate risk controls). If instead it reads “Entropy: ████████░░ 80%”, “Tradeability: ███▒▒▒▒▒▒ 30%”, Mode = Choppy (magenta), the trader knows the market is random and low-momentum—likely best to sit out until conditions improve.
Example: How It Looks in Action
Screenshot 1: Trending Market with High Tradeability (SOLUSD, 30m)
What it means:
The market is in a clear, strong trend with excellent conditions for trading. Both trend-following and active strategies are favored, supported by high tradeability and strong volume.
Screenshot 2: Optimal Regime, Strong Trend (ETHUSD, 1h)
What it means:
This is an ideal environment for trend trading. The market is highly organized, tradeability is excellent, and volume supports the move. This is when the indicator signals the highest probability for success.
Screenshot 3: Choppy Market with High Volume (BTC Perpetual, 5m)
What it means:
The market is highly random and choppy, despite a surge in volume. This is a high-risk, low-reward environment, avoid trend strategies, and be cautious even with mean-reversion or scalping.
Settings and Inputs
The script is fully open-source; here are key inputs the user can adjust:
Entropy Window (len_entropy) : Number of bars used for entropy and tradeability (default 50). Larger = smoother, more lag; smaller = more sensitivity.
Rhythm Window (len_rhythm ): Bars used for cycle detection (default 30). This limits the longest cycle we detect.
Dashboard Position : Choose any corner (Top Right default) so it doesn’t cover chart action.
Show Heatmap : Toggles the entropy background coloring on/off.
Heatmap Style : “Neon Rainbow” (colorful) or “Classic” (green→red).
Show Mode Bar : Turn the bottom mode bar on/off.
Show Dashboard : Turn the fixed table panel on/off.
Each setting has a tooltip explaining its effect. In the description we will mention typical settings (e.g. default window sizes) and that the user can move the dashboard corner as desired.
Oscillator Interpretation (Recap)
Lines : Blue/Pink = Entropy (low=trend, high=chop); Green/Yellow = Tradeability (low=quiet, high=volatile).
Fill : Orange tinted area between them (for visual emphasis).
Bars : Green=Trending, Magenta=Choppy, Cyan=Neutral (at bottom).
Star Line : White star = ideal conditions, Gold = good but not ideal.
Horizontal Guides : 0.2 and 0.8 lines mark low/high thresholds for each metric.
Using the chart, a coder or trader can see exactly what each output represents and make decisions accordingly.
Disclaimer
This indicator is provided as-is for educational and analytical purposes only. It does not guarantee any particular trading outcome. Past market patterns may not repeat in the future. Users should apply their own judgment and risk management; do not rely solely on this tool for trading decisions. Remember, TradingView scripts are tools for market analysis, not personalized financial advice. We encourage users to test and combine MERV with other analysis and to trade responsibly.
-BullByte
Order Blocks with Volume Heatmap & Clusters - VK TradingOrder Blocks with Volume Heatmap & Clusters - VK Trading
This script is designed to identify and highlight Order Blocks, a key concept in institutional trading, and combines it with powerful tools like volume heatmaps and accumulation clusters for enhanced market analysis. Suitable for traders of all experience levels, this script provides a clear and customizable visualization to help identify significant market zones effectively.
What Does This Script Do?
Order Block Identification: Highlights bullish and bearish order blocks directly on the chart, making it easier to spot key supply and demand zones.
Volume Heatmap: A dynamic heatmap adjusts colors based on relative volume, allowing you to quickly identify areas of heightened activity.
Institutional Accumulation Clusters: Zones of potential institutional accumulation are calculated using a combination of ATR (Average True Range), standardized volume, and RSI (Relative Strength Index).
Automatic Clearing: Invalidated order blocks are automatically removed, ensuring your charts remain clean and focused.
Key Features
Customizable Sensitivity: Adjust the script’s sensitivity to tailor order block detection to different market conditions and strategies.
Advanced Volume Display Options: Toggle volume visibility on or off. Customize the position, size, and color of volume labels for better integration with your chart's design.
Dynamic Heatmap Intensity: Fine-tune the heatmap’s intensity and color to highlight areas of interest based on trading volume.
Dual Order Block Detection: Uses two independent detection settings to analyze the market from multiple perspectives.
Visual Alerts: Automatically draws key level lines based on detected order blocks for better clarity.
User Benefits:
Clear Market Analysis: Helps pinpoint institutional activity and key levels with minimal effort.
Increased Efficiency: Automates plotting and analysis, allowing you to focus on decision-making.
Versatile Compatibility: Complements strategies like Smart Money Concepts, Wyckoff, and Price Action approaches.
Disclaimer
This script is intended as an analytical and educational tool. It does not guarantee specific outcomes or eliminate trading risks. Use this tool at your own discretion and always practice proper risk management.
OrbitPips - Hour-of-Week Bias Heatmap v2.1OrbitPips — Hour-of-Week Bias Heatmap v2.1
Hourly return-bias analytics for any symbol, optimised for Mon–Fri markets (weekend toggle available).
A compact 5 × 24 heat-map shows average log-return per hour, t-score significance (★ / ★★), and a day-summary column so you can spot which weekdays and hours statistically favour bullish or bearish moves.
Key Features
• Bias colour-map — neon turquoise ⇒ positive, orange-red ⇒ negative
• Significance stars — ★ if |t| ≥ threshold, ★★ if |t| ≥ 1.5 × threshold
• Day aggregate column — one-look view of each weekday’s net bias
• Coverage % — data-quality gauge vs theoretical max bars
• Weekends toggle — expand to 7 × 24 grid for crypto / 24-7 markets
• Brand palette + large font default — clean cyber-noir theme out of the box
User Inputs (most common)
weeks – 104 (look-back window)
minSamples – 5 (grey-out thin data)
tThreshold – 2.0 (star cutoff)
showWeekends – false (include Sat–Sun rows)
fontScale – Large (UI text size)
How to Read
1. Brighter turquoise → statistically higher average return for that hour.
2. Brighter orange-red → statistically negative return.
3. ★ / ★★ mark cells where the bias is statistically significant.
4. Right-most column aggregates the whole day; compare Monday vs Friday.
5. Check Coverage % in the Analytics panel—low coverage means you should shorten the look-back or relax minSamples .
Changelog (v2.1)
– Adjustable t-score threshold + double-star
– Added per-weekday summary column
– Analytics shows Data Coverage %
– Brand colour palette & larger default font
Disclaimer
This script is for informational and educational purposes only and does not provide trade signals. Past statistical bias does not guarantee future performance. Use at your own risk.
Made with ❤️ by OrbitPips — orbitpips.com
RSI Divergence (Nikko)RSI Divergence by Nikko
🧠 RSI Divergence Detector — Nikko Edition This script is an enhanced RSI Divergence detector built with Pine Script v6, modified for better visuals and practical usability. It uses linear regression to detect bullish and bearish divergences between the RSI and price action — one of the most reliable early signals in technical analysis.
✅ Improvements from the Original:
- Clean divergence lines using regression fitting.
- Optional label display to reduce clutter (Display Labels toggle).
- Adjustable line thickness (Display Line Width).
- A subtle heatmap background to highlight RSI overbought/oversold zones.
- Uses max accuracy with high calc_bars_count and custom extrapolation window.
🔍 How It Works: The script applies linear regression (least squares method) on both RSI data, and Price (close) data.
It then compares the direction of RSI vs. direction of Price over a set length. If price is making higher highs while RSI makes lower highs, it's a bearish divergence. If price is making lower lows while RSI makes higher lows, it's a bullish divergence. Additional filters (e.g., momentum and slope thresholds) are used to validate only strong divergences.
🔧 Input Parameters: RSI Length: The RSI period (default: 14). RSI Divergence Length: The lookback period for regression (default: 25). Source: Which price data to calculate RSI from (default: close). Display Labels: Show/hide “Bullish” or “Bearish” labels on the chart. Display Line Width: Adjusts how thick the plotted divergence lines appear.
📣 Alerts: Alerts are built-in for both RSI Buy (bullish divergence) and RSI Sell (bearish divergence) so you can use it in automation or notifications.
🚀 Personal Note: I’ve been using this script daily in my own trading, which is why I took time to improve both the logic and visual clarity. If you want a divergence tool that doesn't clutter your chart but gives strong signals, this might be what you're looking for.
Heatmap Trailing Stop with Breakouts (Zeiierman)█ Overview
Heatmap Trailing Stop with Breakouts (Zeiierman) is a trend and breakout detection tool that combines dynamic trailing stop logic, Fibonacci-based levels, and a real-time market heatmap into a single, intuitive system.
This indicator is designed to help traders visualize pressure zones, manage stop placement, and identify breakout opportunities supported by contextual price–derived heat. Whether you're trailing trends, detecting reversals, or entering on explosive breakouts — this tool keeps you anchored in structure and sentiment.
It projects adaptive trailing stop levels and calculates Fibonacci extensions from swing-based extremes. These levels are then colored by a market heatmap engine that tracks price interaction intensity — showing where the market is "hot" and likely to respond.
On top of that, it includes breakout signals powered by HTF momentum conditions, trend direction, and heatmap validation — giving you signals only when the context is strong.
█ How It Works
⚪ Trailing Stop Engine
At its core, the script uses an ATR-based trailing stop with trend detection:
ATR Length – Defines volatility smoothing using EMA MA of true range.
Multiplier – Expands/retracts the trailing offset depending on market aggression.
Real-Time Extremum Tracking – Uses local highs/lows to define Fibonacci anchors.
⚪ Fibonacci Projection + Heatmap
With each trend shift, Fibonacci levels are projected from the new swing to the current trailing stop. These include:
Fib 61.8, 78.6, 88.6, and 100% (trailing stop) lines
Heatmap Coloring – Each level'slevel's color is determined by how frequently price has interacted with that level in the recent range (defined by ATR).
Strength Score (1–10) – The number of touches per level is normalized and averaged to create a heatmap ""score"" displayed as a colored bar on the chart.
⚪ Breakout Signal System
This engine detects high-confidence breakout signals using a higher timeframe candle structure:
Bullish Breakout – Strong bullish candle + momentum + trend confirmation + heatmap score threshold.
Bearish Breakout – Strong bearish candle + momentum + trend confirmation + heatmap score threshold.
Cooldown Logic – Prevents signals from clustering too frequently during volatile periods.
█ How to Use
⚪ Trend Following & Trail Stops
Use the Trailing Stop line to manage positions or time entries in line with trend direction. Trailing stop flips are highlighted with dot markers.
⚪ Fibonacci Heat Zones
The projected Fibonacci levels serve as price magnets or support/resistance zones. Watch how price reacts at Fib 61.8/78.6/88.6 levels — especially when they're glowing with high heatmap scores (more glow = more historical touches = stronger significance).
⚪ Breakout Signals
Enable breakout signals when you want to trade breakouts only under strong context. Use the "Heatmap Strength Threshold" to require a minimum score (1–10).
█ Settings
Stop Distance ATR Length – ATR period for volatility smoothing
Stop Distance Multiplier – Adjusts the trailing stop'sstop's distance from price
Heatmap Range ATR Length – Defines how far back the heatmap scans for touches
Number of Heat Levels – Total levels used in the heatmap (more = finer resolution)
Minimum Touches per Level – Defines what counts as a ""hot"" level
Heatmap Strength Threshold – Minimum average heat score (1–10) required for breakouts
Timeframe – HTF source used to evaluate breakout momentum structure
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Ultra VolumeVisualizes volume intensity using dynamic color gradients and percentile thresholds. Includes optional SMA, bar coloring, and adaptive liquidity boxes to highlight high- and low-volume zones in real time.
Introduction
The Ultra Volume indicator enhances volume analysis by categorizing volume bars into percentile-based intensity levels. It uses color-coded gradients to quickly identify periods of unusually high or low activity. The script also includes an optional simple moving average (SMA), bar coloring, and visual box overlays to highlight zones of significant liquidity shifts.
Detailed Description
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Volume Classification
Volume is segmented into five tiers: Extra High, High, Medium, Normal, and Low, using percentile ranks calculated over a dynamically adjusted historical window. This segmentation adapts based on the chart's timeframe – using 100 bars for daily and 1440/minutes for intraday – allowing for consistent behavior across resolutions.
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Color Gradients
Each volume bar is colored based on its percentile category, smoothly transitioning between thresholds for visual clarity. This makes it easy to spot volume spikes or droughts relative to recent history.
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Simple Moving Average (SMA)
An optional SMA can be plotted on top of the volume bars for trend comparison and baseline reference. Its length and color are fully customizable.
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Bar Coloring
You can optionally color the chart's candlesticks to reflect the same volume intensity as the histogram bars, reinforcing visual cues across the chart.
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Liquidity Boxes
Two adaptive box systems highlight zones of increased or decreased liquidity:
High Liquidity Boxes expand upward when price exceeds the previous box’s top.
Low Liquidity Boxes expand downward when price breaks the previous box’s bottom.
These boxes persist and auto-adjust over time unless reset, helping traders spot key zones of volume-driven price action.
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Box Indexing
A configurable index shift determines how far back in the chart the boxes originate. Setting this to 501 makes them "stick" to the candle where they were first created.
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Data Handling
A safety check ensures the script throws an error if volume data is unavailable (e.g., for some crypto or CFD symbols).
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Summary
Ultra Volume is a practical tool for traders who want more than just raw volume bars. With intelligent percentile-based classification, real-time adaptive liquidity zones, and fully customizable visual elements, it turns volume into a highly readable, actionable signal.
Liquidation Heatmap ║ BullVision 🧠Overview
The Liquidation Heatmap ║ BullVision 💥 is a high-precision visualization tool engineered to highlight probable liquidation levels in crypto markets. It leverages multi-exchange Open Interest data, real-time volume dynamics, and structure-aware volatility signals to reveal where leveraged traders are most at risk of forced position closures.
📖 What Are Liquidations?
In leveraged derivatives markets, a liquidation occurs when a trader’s margin becomes insufficient to maintain their position, triggering an automatic force-close by the exchange. These events are typically clustered around price levels where large volumes of overleveraged positions accumulate. When breached, they often result in sharp, aggressive price movements — also known as liquidation cascades.
This indicator is designed to detect and project such high-risk zones before they trigger, giving traders an edge in visualizing hidden pressure points in the market.
🧠 How It Works
The core engine aggregates real-time ∆OI (Open Interest delta) data from multiple major exchanges and applies a layered filtering system that considers:
Relative Open Interest shifts, normalized against an adaptive moving average baseline
Volume acceleration patterns, compared to a rolling historical benchmark
Market structure context, identifying meaningful directional breaks and failed retests
Leverage-tier modeling, using probabilistic distance rules to simulate where liquidations from 5x to 100x positions would be triggered
Each qualifying liquidation level is rendered using dynamic gradient lines and optional glow-enhanced zone visuals. The display adapts in real time to structural confirmation, volatility regime, and liquidity depth.
Exemple of Liquidation cascades
Exemple of Liquidation rejection
🔍 Key Features
🔗 Multi-Exchange OI Aggregation: Binance, OKX, BitMEX, Kraken (toggleable)
📊 Leverage-Tier Mapping: 5x, 10x, 25x, 50x, 100x projections
🎨 Gradient Zones: Custom color ramps reflect level significance
🧱 Structure-Sensitive Filtering: Noise reduction via multi-condition confirmation logic
🧠 Contextual Directional Bias: Zones filtered based on recent bullish/bearish transitions
⚙️ Fully Customizable: User-defined intensity thresholds, color palette, and range filtering
🧩 Why It’s Worth Paying For
This is not a mashup of public indicators. The script introduces an original, multi-layered architecture combining real-time Open Interest dynamics, structural analysis, and custom liquidation modeling.
Unlike speculative support/resistance plots or volume-only heatmaps, this tool is built to:
Detect liquidation zones before they cascade
React dynamically to market shifts
Filter noise through structural confirmation
Retain historical zones for visual learning and backtesting
✅ Compliance & Originality
This script was developed entirely in-house with original detection logic. No reused open-source components are included. Data requests are made through TradingView’s native .P_OI feeds, and all calculations, signal conditions, and visual logic were coded from scratch for this script.
⚠️ Risk Disclaimer & Access Policy
This script is a visual risk-awareness tool, not a signal generator or financial advice mechanism. No guarantee is made regarding future price action, liquidation triggers, or trading performance.
Use at your own discretion, with proper position sizing, risk management, and awareness of the market's inherent uncertainty.
🔒 Why This Script Is Invite-Only and Closed-Source
To protect its proprietary detection engine, this script is both closed-source and invite-only. The algorithm uses original methods to:
Aggregate real-time Open Interest delta across exchanges
Simulate leverage-based liquidation zones
Dynamically filter zones using structure and volatility layers
Opening the source would expose core detection logic to copycats or misuse. Likewise, access is limited to ensure the tool is used responsibly by serious traders and not distributed or repackaged unethically.
This model preserves the script’s quality, originality, and intended value.
SUPER-MAGFLXMAGFLX
Made a bunch of these for different sectors, then realized they’re all basically the same—so you really only need one.
Here it is, with a few extra features like customizable display position and metric options.
Track 1 to 20+ tickers, your way, all in one clean, versatile template.
Features & Uses
Custom Ticker List: Enter any tickers you want to track—mix and match sectors or asset classes freely.
Flexible Display: Choose where the table appears on your chart (top-right, top-left, bottom-right, bottom-left).
Metric Options: Toggle on/off daily percentage change, current price, and price difference columns based on what you want to monitor.
Highlight Movers: Automatically spot and highlight the biggest gainer and biggest loser each day for quick insights.
Compact & Efficient: Fits neatly on your chart without clutter, whether tracking 1 ticker or 20+.
Color-Coded Data: Intuitive colors make it easy to spot gains, losses, and key movers at a glance.
User-Friendly: No coding needed—simply input your tickers and preferences to tailor your watchlist instantly.
Use it to:
Monitor your portfolio across multiple sectors in one place.
Quickly spot daily winners and losers.
Keep an eye on price trends and changes without opening multiple charts.
Save chart space while gaining market clarity.
Any comments welcomed there is no way to tell if a public script is being used right ? so if you use and like it give it boost or a comment to let me know
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets.
Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium — offering actionable signals for mean-reversion trades .
What Is Cointegration?
Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow — it doesn’t imply a stable or predictable relationship over time.
Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary.
Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator.
This tool helps answer key questions like:
“Which stocks tend to move in sync over the long term?”
“When are two assets diverging beyond statistical norms?”
“Is now the right time to short one and long the other?”
Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back — making it ideal for building low-risk, high-probability trade setups.
In simple terms:
Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading.
How the Indicator Works
Cointegration Heatmap indicator works across any market supported on TradingView — from stocks and ETFs to cryptocurrencies and forex pairs.
You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights.
Indicator Settings:
Symbol list: a customizable list of symbols separated by commas
Returns timeframe: time frame selection for return sampling (Weekly or Monthly)
Max periods: max periods to limit the data to a certain time and to control indicator performance
This indicator accomplishes three major goals in one streamlined package:
Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization.
Tracks the spread — the difference between actual prices and the predicted linear relationship — between each pair.
Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert.
The math:
Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies.
For each unique pair of symbols, we perform a linear regression
Yt=α+βXt+ε
Then we compute the residuals (errors from the regression):
Spreadt=Yt−(α+βXt)
Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score:
S=1/Standard Deviation of Residuals
This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration.
Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision.
How to use the indicator
The heatmap table:
The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table.
Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable.
Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship.
Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is.
The spread table:
After finding a good asset pair using heatmap, locate the same pair in the spread table (right side).
Here’s what you’ll see on the table:
Spread: Current difference between the two symbols based on the regression fit
Mean: Historical average of that spread
Z-score: How far current spread is from the mean in standard deviations
Signal: Trade suggestion: Short, Long, or Neutral
Since you’re expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0.
You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level.
Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X).
In this case, STOCKB is the quoted asset and STOCKA is the base asset.
In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
Liqudation HeatMap [BigBeluga]🔵 OVERVIEW
An advanced liquidity visualization tool that plots horizontal heat zones to highlight where potential liquidations and volume clusters are most likely hiding beneath price action.
Liqudation HeatMap scans historical price movements for local highs and lows with elevated volume or candle range. It then draws dynamic heatmap boxes—shaded from lime (low interest) to yellow (high interest)—revealing potential zones of trapped positions or stop clusters. A vertical scale on the right shows you the relative strength of volume behind each level, from 0 to the highest detected.
🔵 CONCEPTS
Maps areas of potential liquidity using volume or candle range (if volume is unavailable).
Identifies swing highs/lows (pivots) and extends heatmap boxes outward from these levels. Colors each zone based on the relative strength of volume concentration.
Fades or removes zones once price crosses their midpoints, simulating the idea of liquidity being “consumed.”
Displays a live vertical scale that shows the volume range for quick reference.
🔵 FEATURES
Dynamic Heatmap Zones:
Draws few boxes above and after pivot highs and below pivot lows, each shaded based on volume concentration.
Smart Coloring System:
Uses a gradient from lime (low) to yellow (high) to visually distinguish between weak and strong liquidity zones.
Adaptive ATR Widths:
Automatically adjusts zone thickness based on volatility (ATR), scaling intelligently across timeframes.
Liquidity Consumption Logic:
Zones are stope extending once price interacts with them—mimicking the behavior of real liquidation sweeps.
Volume Scale Legend:
A real-time scale is plotted on the right side, showing the min-max range of volume used for heat calculations.
🔵 HOW TO USE
Look for thick yellow zones to identify areas of concentrated stop losses or liquidation triggers.
Use these levels to anticipate mean reversion points or high-volatility zones.
Combine with your trend or structure tools to trade into or fade these liquidity pools.
On lower timeframes, use this tool to confirm entries around sweeps or deviations.
Use the right-side scale to compare relative zone strength instantly.
🔵 CONCLUSION
Liqudation HeatMap is a powerful visualization tool that uncovers where liquidity likely resides on the chart. By highlighting hidden traps and reactive levels in real-time, it gives traders a significant edge when it comes to spotting stop hunts, mean reversions, and areas of institutional interest. Whether you’re scalping or swing trading, this heatmap provides unmatched context on the market’s hidden intent.
Supply & Demand Histogram and Lines [BerlinCode42]Happy Trade,
This is a Supply & Demand Histogram—also referred to as a Heatmap—that highlights key S&D levels on the chart. Unlike traditional approaches that use volume, this script identifies specific chart patterns and evaluates them to generate the Supply & Demand Histogram. It analyzes the Supply and the Demand separately.
The script is equipped with trade signals for external use (Indicator on Indicator) and is fully compatible with my strategy template script. This allows you to easily create backtests and combine it with other indicators to build a custom strategy.
Intro
Calculation of the Supply & Demand Histogram
Usage and Settings Menu
Declaration for Tradingview House Rules on Script Publishing
Disclaimer
1. Calculation of the Supply & Demand Histogram
Initially, the total price range—spanning from the absolute minimum to the absolute maximum observed price—is discretized into 10,000 equally sized intervals. For each interval, the algorithm performs the following:
It detects chart patterns that typically emerge in zones of varying volatility, categorizing them accordingly. Each identified pattern is assigned a individual weight based on its structural parameters, such as amplitude or slope. Lets call them Structural Weights. These weighted occurrences are then aggregated per interval, resulting in a quantitative representation of supply and demand pressure across the price spectrum, visualized as a histogram.
This pattern-based methodology facilitates the quantitative estimation of supply and demand zones without reliance on volume metrics.
2. Usage and Settings Menu
Initially, the user can configure the granularity of the price segmentation used in the Supply & Demand Histogram. This is achieved by enabling the 'Show Price Range' option, as illustrated in Image 1. Activating this feature overlays a gray-shaded region on the chart, visually representing the defined price range.
Image 1
The vertical position of this range can be adjusted using the 'Price Range Offset' parameter, while the interval widths are modifiable via the 'Step Factor' setting. It is critical to ensure that the specified range encapsulates the entirety of historical and anticipated price movements; failure to do so may result in calculation errors if price action extends beyond the defined bounds. Nevertheless, the default Step Factor has been conservatively chosen to accommodate most price dynamics.
Due to performance considerations, the indicator does not render all 10,000 discrete intervals comprising the full histogram. Instead, it selectively displays a subset of 100 intervals centered around the most recent price."
Once the price range has been configured, disable the “Show Price Range” option again in order to display the Supply & Demand Histogram.
Subsequently, users can fine-tune the histogram computation via two key settings, shown in Image 2:
Volume Count – This option allows selection between a pattern-based structural weighting method and a traditional volume-based approach for histogram construction. The structural method estimates significance through pattern characteristics rather than traded volume.
Supply + Demand – This toggle determines whether Supply and Demand levels are calculated and displayed independently or merged into a unified histogram. If one subscribes to the principle that a breached Supply zone can transform into a Demand zone (and vice versa), enabling this option will reflect that assumption by aggregating both into a single composite structure.
Image 2
Once this setup is complete, the Supply & Demand Histogram along with its most significant price levels will be visualized on the chart. Users can further refine the display settings to tailor the visual output.
In the settings menu, refer to the section illustrated in Image 3. There, you can adjust the number of displayed price levels by increasing or decreasing the S&D Line Filter percentage. A lower percentage results in fewer, more prominent levels being shown, while a higher percentage includes more levels.
The S&D histogram itself can also be hidden if desired.
Image 3
This indicator supports external integration via Indicator on Indicator Functionality or alerts. Specifically, when a price level is either touched or broken, an alert can be triggered. To visually identify where such alerts would occur, enable Show Alert Labels, which marks the respective bars on the chart.
If you want to import the trade signals into a Backtest or Strategy Template script, simply use the two signal outputs: "Break Signals" and "Touch Signals".
A value of zero indicates that no touching or breaking event is occurring.
A positive value signifies that a supply level has been touched or broken.
A negative value indicates a demand level interaction.
The absolute value of each signal corresponds to the price level of the respective Supply or Demand line.
The colors used to represent Supply and Demand levels can be customized to your preference.
Additionally, a Time and Session Filter has been added. This feature allows you to exclude specific time periods and dates from the analysis, enabling a better understanding of which trading times and market sessions are responsible for the formation of particular Supply & Demand levels.
To activate the filter, check the leftmost checkbox, then define the desired Date, Time, and Session parameters accordingly as shown in image 4.
Image 4
3. Declaration for Tradingview House Rules on Script Publishing
The unique feature of this Supply & Demand Histogram is its pattern-based calculation methodology. This approach enables the estimation of Supply and Demand levels even for assets that do not provide volume data. Additionally, it allows for separate computation of Supply and Demand. That means a broken Demand level does not necessarily convert into a Supply level, and vice versa.
This script is closed-source and invite-only to support and compensate for months long development work.
4. Disclaimer
Trading is risky, and traders do lose money, eventually all. This script is for informational and educational purposes only. All content should be considered hypothetical, selected post-factum and is not to be construed as financial advice. Decisions to buy, sell, hold, or trade in securities, commodities, and other investments involve risk and are best made based on the advice of qualified financial professionals. Past performance does not guarantee future results. Using this script on your own risk. This script may have bugs and I declare don't be responsible for any losses.
Now it’s your turn!
Stochastic Overlay - Regression Channel (Zeiierman)█ Overview
The Stochastic Overlay – Regression Channel (Zeiierman) is a next-generation visualization tool that transforms the traditional Stochastic Oscillator into a dynamic price-based overlay.
Instead of leaving momentum trapped in a lower subwindow, this indicator projects the Stochastic oscialltor directly onto price itself — allowing traders to visually interpret momentum, overbought/oversold conditions, and market strength without ever taking their eyes off price action.
⚪ In simple terms:
▸ The Bands = The Stochastic Oscillator — but on price.
▸ The Midline = Stochastic 50 level
▸ Upper Band = Stochastic Overbought Threshold
▸ Lower Band = Stochastic Oversold Threshold
When the price moves above the midline → it’s the same as the oscillator moving above 50
When the price breaks above the upper band → it’s the same as Stochastic entering overbought.
When the price reaches the lower band →, think of it like Stochastic being oversold.
This makes market conditions visually intuitive. You’re literally watching the oscillator live on the price chart.
█ How It Works
The indicator layers 3 distinct technical elements into one clean view:
⚪ Stochastic Momentum Engine
Tracks overbought/oversold conditions and directional strength using:
%K Line → Momentum of price
%D Line → Smoothing filter of %K
Overbought/Oversold Bands → Highlight potential reversal zones
⚪ Volatility Adaptive Bands
Dynamic bands plotted above and below price using:
ATR * Stochastic Scaling → Creates wider bands during volatile periods & tighter bands in calm conditions
Basis → Moving average centerline (EMA, SMA, WMA, HMA, RMA selectable)
This means:
→ In strong trends: Bands expand
→ In consolidations: Bands contract
⚪ Regression Channel
Projects trend direction with different models:
Logarithmic → Captures non-linear growth (perfect for crypto or exponential stocks)
Linear → Classic regression fit
Adaptive → Dynamically adjusts sensitivity
Leading → Projects trend further ahead (aggressive mode)
Channels include:
Midline → Fair value trend
Upper/Lower Bounds → Deviation-based support/resistance
⚪ Heatmap - Bull & Bear Power Strength
Visual heatmeter showing:
% dominance of bulls vs bears (based on close > or < Band Basis)
Automatic normalization regardless of timeframe
Table display on-chart for quick visual insight
Dynamic highlighting when extreme levels are reached
⚪ Trend Candlestick Coloring
Bars auto-color based on trend filter:
Above Basis → Bullish Color
Below Basis → Bearish Color
█ How to Use
⚪ Trend Trading
→ Use Band direction + Regression Channel to identify trend alignment
→ Longs favored when price holds above the Basis
→ Shorts favored when price stays below the Basis
→ Use the Bull & Bear heatmap to asses if the bulls or the bears are in control.
⚪ Mean Reversion
→ Look for price to interact with Upper or Lower Band extremes
→ Stochastic reaching OB/OS zones further supports reversals
⚪ Momentum Confirmation
→ Crossovers between %K and %D can confirm continuation or divergence signals
→ Especially powerful when happening at band boundaries
⚪ Strength Heatmap
→ Quickly visualize current buyer vs seller control
→ Sharp spikes in Bull Power = Aggressive buying
→ Sharp spikes in Bear Power = Heavy selling pressure
█ Why It Useful
This is not a typical Stochastic or regression tool. The tool is designed for traders who want to:
React dynamically to price volatility
Map momentum into volatility context
Use adaptive regression channels across trend styles
Visualize bull vs bear power in real-time
Follow trends with built-in reversal logic
█ Settings
Stochastic Settings
Stochastic Length → Period of calculation. Higher = smoother, Lower = faster signals.
%K Smoothing → Smooths the Stochastic line itself.
%D Smoothing → Smooths the moving average of %K for slower signals.
Stochastic Band
Band Length → Length of the Moving Average Basis.
Volatility Multiplier → Controls band width via ATR scaling.
Band Type → Choose MA type (EMA, SMA, WMA, HMA, RMA).
Regression Channel
Regression Type → Logarithmic / Linear / Adaptive / Leading.
Regression Length → Number of bars for regression calculation.
Heatmap Settings
Heatmap Length → Number of bars to calculate bull/bear dominance.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Daily Performance HeatmapThis script displays a customizable daily performance heatmap for key assets across crypto, equities, bonds, commodities, currencies, and volatility indices.
Each cell shows the current price and the percent change since the daily open, color-coded using a gradient from negative to positive. Assets are arranged in a left-to-right, top-down grid, with adjustable layout and styling.
⚙️ Features:
🔢 Displays current price and daily % change
🎨 Color-coded heatmap using customizable gradients
🧱 Adjustable layout: number of columns, cell size, and text size
🧠 Smart price formatting (no decimals for BTC, Gold, etc.)
🪟 Clean alignment with padded spacing for UI clarity
🛠️ Future plans:
User-input asset lists and labels
Category grouping and dynamic sorting
Optional icons, tooltips, or alerts
ZVOL — Z-Score Volume Heatmapⓩ ZVOL transforms raw volume into a statistically calibrated heatmap using Z-score thresholds. Unlike classic volume indicators that rely on fixed MA comparisons, ZVOL calculates how many standard deviations each volume bar deviates from its mean. This makes the reading adaptive across timeframes and assets, in order to distinguish meaningful crowd behavior from random volatility.
📊 The core display is a five-zone histogram, each encoded by color and statistical depth. Optional background shading mirrors these zones across the entire pane, revealing subtle compression or structural rhythm shifts across time. By grounding the volume reading in volatility-adjusted context, ZVOL inhibits impulsive trading tactics by compelling the structure, not the sentiment, to dictate the signal.
🥵 Heatmap Coloration:
🌚 Suppressed volume — congestion, coiling phases
🩱 Stable flow — early trend or resting volume
🏀 High activity — emerging pressure
💔 Extreme — possible climax or institutional print
🎗️ A dynamic Fibonacci-based 21:34-period EMA ribbon overlays the histogram. The fill area inverts color on crossover, providing a real-time read on tempo, expansion, or divergence between price structure and crowd effort.
💡 LTF Usage Suggestions:
• Confirm breakout legs when orange or red zones align with range exits
• Fade overextended moves when red bars appear into resistance
• Watch for rising EMAs and orange volume to front-run impulsive moves
• Combine with volatility suppression (e.g. ATR) to catch compression → expansion transitions
🥂 Ideal Pairings:
• OBVX Conviction Bias — to confirm directional intent behind volume shifts
• SUPeR TReND 2.718 — for directional filters
• ATR Turbulence Ribbon — to detect compression phases
👥 The OBVX Conviction Bias adds a second dimension to ZVOL by revealing whether crowd effort is aligning with price direction or diverging beneath the surface. While ZVOL identifies statistical anomalies in raw volume, OBVX tracks directional commitment using cumulative volume and moving average cross logic. Use them together to spot fake-outs, anticipate structure-confirmed breakouts, or time pullbacks with volume-based conviction.
🔬 ZVOL isn’t just a volume filter — it’s a structural lens. It reveals when crowd effort is meaningful, when it's fading, and when something is about to shift. Designed for structure-aware traders who care about context, not noise.
Correlation Heatmap█ OVERVIEW
This indicator creates a correlation matrix for a user-specified list of symbols based on their time-aligned weekly or monthly price returns. It calculates the Pearson correlation coefficient for each possible symbol pair, and it displays the results in a symmetric table with heatmap-colored cells. This format provides an intuitive view of the linear relationships between various symbols' price movements over a specific time range.
█ CONCEPTS
Correlation
Correlation typically refers to an observable statistical relationship between two datasets. In a financial time series context, it usually represents the extent to which sampled values from a pair of datasets, such as two series of price returns, vary jointly over time. More specifically, in this context, correlation describes the strength and direction of the relationship between the samples from both series.
If two separate time series tend to rise and fall together proportionally, they might be highly correlated. Likewise, if the series often vary in opposite directions, they might have a strong anticorrelation . If the two series do not exhibit a clear relationship, they might be uncorrelated .
Traders frequently analyze asset correlations to help optimize portfolios, assess market behaviors, identify potential risks, and support trading decisions. For instance, correlation often plays a key role in diversification . When two instruments exhibit a strong correlation in their returns, it might indicate that buying or selling both carries elevated unsystematic risk . Therefore, traders often aim to create balanced portfolios of relatively uncorrelated or anticorrelated assets to help promote investment diversity and potentially offset some of the risks.
When using correlation analysis to support investment decisions, it is crucial to understand the following caveats:
• Correlation does not imply causation . Two assets might vary jointly over an analyzed range, resulting in high correlation or anticorrelation in their returns, but that does not indicate that either instrument directly influences the other. Joint variability between assets might occur because of shared sensitivities to external factors, such as interest rates or global sentiment, or it might be entirely coincidental. In other words, correlation does not provide sufficient information to identify cause-and-effect relationships.
• Correlation does not predict the future relationship between two assets. It only reflects the estimated strength and direction of the relationship between the current analyzed samples. Financial time series are ever-changing. A strong trend between two assets can weaken or reverse in the future.
Correlation coefficient
A correlation coefficient is a numeric measure of correlation. Several coefficients exist, each quantifying different types of relationships between two datasets. The most common and widely known measure is the Pearson product-moment correlation coefficient , also known as the Pearson correlation coefficient or Pearson's r . Usually, when the term "correlation coefficient" is used without context, it refers to this correlation measure.
The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between two variables. In other words, it indicates how consistently variables' values move together or in opposite directions in a proportional, linear manner. Its formula is as follows:
𝑟(𝑥, 𝑦) = cov(𝑥, 𝑦) / (𝜎𝑥 * 𝜎𝑦)
Where:
• 𝑥 is the first variable, and 𝑦 is the second variable.
• cov(𝑥, 𝑦) is the covariance between 𝑥 and 𝑦.
• 𝜎𝑥 is the standard deviation of 𝑥.
• 𝜎𝑦 is the standard deviation of 𝑦.
In essence, the correlation coefficient measures the covariance between two variables, normalized by the product of their standard deviations. The coefficient's value ranges from -1 to 1, allowing a more straightforward interpretation of the relationship between two datasets than what covariance alone provides:
• A value of 1 indicates a perfect positive correlation over the analyzed sample. As one variable's value changes, the other variable's value changes proportionally in the same direction .
• A value of -1 indicates a perfect negative correlation (anticorrelation). As one variable's value increases, the other variable's value decreases proportionally.
• A value of 0 indicates no linear relationship between the variables over the analyzed sample.
Aligning returns across instruments
In a financial time series, each data point (i.e., bar) in a sample represents information collected in periodic intervals. For instance, on a "1D" chart, bars form at specific times as successive days elapse.
However, the times of the data points for a symbol's standard dataset depend on its active sessions , and sessions vary across instrument types. For example, the daily session for NYSE stocks is 09:30 - 16:00 UTC-4/-5 on weekdays, Forex instruments have 24-hour sessions that span from 17:00 UTC-4/-5 on one weekday to 17:00 on the next, and new daily sessions for cryptocurrencies start at 00:00 UTC every day because crypto markets are consistently open.
Therefore, comparing the standard datasets for different asset types to identify correlations presents a challenge. If two symbols' datasets have bars that form at unaligned times, their correlation coefficient does not accurately describe their relationship. When calculating correlations between the returns for two assets, both datasets must maintain consistent time alignment in their values and cover identical ranges for meaningful results.
To address the issue of time alignment across instruments, this indicator requests confirmed weekly or monthly data from spread tickers constructed from the chart's ticker and another specified ticker. The datasets for spreads are derived from lower-timeframe data to ensure the values from all symbols come from aligned points in time, allowing a fair comparison between different instrument types. Additionally, each spread ticker ID includes necessary modifiers, such as extended hours and adjustments.
In this indicator, we use the following process to retrieve time-aligned returns for correlation calculations:
1. Request the current and previous prices from a spread representing the sum of the chart symbol and another symbol ( "chartSymbol + anotherSymbol" ).
2. Request the prices from another spread representing the difference between the two symbols ( "chartSymbol - anotherSymbol" ).
3. Calculate half of the difference between the values from both spreads ( 0.5 * (requestedSum - requestedDifference) ). The results represent the symbol's prices at times aligned with the sample points on the current chart.
4. Calculate the arithmetic return of the retrieved prices: (currentPrice - previousPrice) / previousPrice
5. Repeat steps 1-4 for each symbol requiring analysis.
It's crucial to note that because this process retrieves prices for a symbol at times consistent with periodic points on the current chart, the values can represent prices from before or after the closing time of the symbol's usual session.
Additionally, note that the maximum number of weeks or months in the correlation calculations depends on the chart's range and the largest time range common to all the requested symbols. To maximize the amount of data available for the calculations, we recommend setting the chart to use a daily or higher timeframe and specifying a chart symbol that covers a sufficient time range for your needs.
█ FEATURES
This indicator analyzes the correlations between several pairs of user-specified symbols to provide a structured, intuitive view of the relationships in their returns. Below are the indicator's key features:
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab accepts a comma-separated list of symbols or ticker identifiers with optional spaces (e.g., "XOM, MSFT, BITSTAMP:BTCUSD"). The indicator dynamically requests returns for each symbol in the list, then calculates the correlation between each pair of return series for its heatmap display.
Each item in the list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script raises a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Note that the number of symbols allowed in the list depends on the user's plan. Users with non-professional plans can compare up to 20 symbols with this indicator, and users with professional plans can compare up to 32 symbols.
Timeframe and data length selection
The "Returns timeframe" input specifies whether the indicator uses weekly or monthly returns in its calculations. By default, its value is "1M", meaning the indicator analyzes monthly returns. Note that this script requires a chart timeframe lower than or equal to "1M". If the chart uses a higher timeframe, it causes a runtime error.
To customize the length of the data used in the correlation calculations, use the "Max periods" input. When enabled, the indicator limits the calculation window to the number of periods specified in the input field. Otherwise, it uses the chart's time range as the limit. The top-left corner of the table shows the number of confirmed weeks or months used in the calculations.
It's important to note that the number of confirmed periods in the correlation calculations is limited to the largest time range common to all the requested datasets, because a meaningful correlation matrix requires analyzing each symbol's returns under the same market conditions. Therefore, the correlation matrix can show different results for the same symbol pair if another listed symbol restricts the aligned data to a shorter time range.
Heatmap display
This indicator displays the correlations for each symbol pair in a heatmap-styled table representing a symmetric correlation matrix. Each row and column corresponds to a specific symbol, and the cells at their intersections correspond to symbol pairs . For example, the cell at the "AAPL" row and "MSFT" column shows the weekly or monthly correlation between those two symbols' returns. Likewise, the cell at the "MSFT" row and "AAPL" column shows the same value.
Note that the main diagonal cells in the display, where the row and column refer to the same symbol, all show a value of 1 because any series of non-na data is always perfectly correlated with itself.
The background of each correlation cell uses a gradient color based on the correlation value. By default, the gradient uses blue hues for positive correlation, orange hues for negative correlation, and white for no correlation. The intensity of each blue or orange hue corresponds to the strength of the measured correlation or anticorrelation. Users can customize the gradient's base colors using the inputs in the "Color gradient" section of the "Settings/Inputs" tab.
█ FOR Pine Script® CODERS
• This script uses the `getArrayFromString()` function from our ValueAtTime library to process the input list of symbols. The function splits the "string" value by its commas, then constructs an array of non-empty strings without leading or trailing whitespaces. Additionally, it uses the str.upper() function to convert each symbol's characters to uppercase.
• The script's `getAlignedReturns()` function requests time-aligned prices with two request.security() calls that use spread tickers based on the chart's symbol and another symbol. Then, it calculates the arithmetic return using the `changePercent()` function from the ta library. The `collectReturns()` function uses `getAlignedReturns()` within a loop and stores the data from each call within a matrix . The script calls the `arrayCorrelation()` function on pairs of rows from the returned matrix to calculate the correlation values.
• For consistency, the `getAlignedReturns()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences.
• A Pine script can execute up to 40 or 64 unique `request.*()` function calls, depending on the user's plan. The maximum number of symbols this script compares is half the plan's limit, because `getAlignedReturns()` uses two request.security() calls.
• This script can use the request.security() function within a loop because all scripts in Pine v6 enable dynamic requests by default. Refer to the Dynamic requests section of the Other timeframes and data page to learn more about this feature, and see our v6 migration guide to learn what's new in Pine v6.
• The script's table uses two distinct color.from_gradient() calls in a switch structure to determine the cell colors for positive and negative correlation values. One call calculates the color for values from -1 to 0 based on the first and second input colors, and the other calculates the colors for values from 0 to 1 based on the second and third input colors.
Look first. Then leap.
Sector ETFsSector ETFs
Cool unobtrusive way to keep your eye on the market or tickers of your choice without leaving your chart - Can keep you clued into relative strength and weakness between sectors as well as sector rotation.
This script tracks the percentage changes of a list of Sector ETFs and displays the results in a table on the chart. It also triggers an alert when all selected ETFs are either positive (green) or negative (red).
Key Features
1. Input: Users can amend the list of ETF symbols and basically fill the table with tickers of their preferred stocks
2. Percentage Change: Calculates the daily percentage change for each ETF or chosen stock
3. Color-Coding: This script is live in real time and dynamic the ETFs will be green if higher than the previous close (positive change), really bright green (>=10%), or red if lower than the previous close (negative change).
4. Table displays ETFs and their percentage changes at the top-right of the chart.
5. Alert Condition: Triggers an alert when all ETFs are simultaneously green or simultaneously red - this is done by right clicking on the table or going into settings. please note there will be a TV caution due to an indictor that can be repainted
How to Use
1. Add the script to your TradingView chart.
2. Keep or customize the ETF list by editing the input field.
3. The table will show each ETF's change and color-coded performance.
4. Set alerts based on the condition "All ETFs Turned Green or Red".
Also note pre and post market movements will not be captured by this indicator (did try does not appear to be possible - Pine Script limitation ) all movement is in comparison to prior close in regular market hours .
Does work in replay mode
Enjoy - Hope it helps with your trading !
liquidation Heatmap [by Alpha_Precision_Charts]Indicator Description: Heatmap Longs/Shorts with OI Sensitivity & Aggregated Tools
Overview
The "Heatmap Longs/Shorts with OI Sensitivity & Aggregated Tools" is an advanced, multi-functional indicator crafted for futures traders seeking a deeper understanding of market dynamics. This tool integrates several key features—Heatmap of Longs and Shorts with Open Interest (OI) sensitivity, Histograms, Liquidity Exit Bubbles, Volume Bubbles, RSI Labels, Moving Averages, and an OI Table—into a single, cohesive package. By pulling real-time OI data from major exchanges (Binance, BitMEX, OKX, Kraken), it offers a robust framework for analyzing liquidity, order flow, momentum, and trends across various timeframes.
Why Aggregation Matters
Market analysis thrives on combining diverse insights, as relying on a single tool often leaves gaps in understanding. Each component of this indicator addresses a distinct aspect of market behavior:
Heatmap Longs/Shorts with OI Sensitivity: Maps potential liquidation zones based on OI, pinpointing where leveraged positions might cluster.
Histograms: Visualize the density of potential liquidity across price levels, enhancing OI-based analysis.
OI Table: Provides a breakdown of OI across all supported exchanges, offering transparency into total market exposure.
Liquidity Exit Bubbles: Highlight significant position exits (negative OI delta), signaling potential reversals or liquidations.
Volume Bubbles: Detect high-volume events from perpetual futures, revealing aggressive market participation.
RSI Labels: Track momentum with overbought and oversold conditions, refining entry and exit timing.
Moving Averages: Establish trend direction and dynamic support/resistance levels.
The power of aggregation lies in its ability to connect these dots. For instance, the Heatmap identifies potential liquidation zones, Volume Bubbles confirm aggressive moves, and RSI Labels add momentum context. Histograms and the OI Table further enrich this by detailing liquidity density and market exposure, creating a comprehensive view critical for navigating volatile markets.
Key Features
Heatmap Longs/Shorts with OI Sensitivity
Displays potential liquidation levels above (Shorts) and below (Longs) the price, with leverage settings from 5x to 125x.
Includes a Minimum Liquidity Sensitivity filter (0.1-1.0) to exclude small-order noise.
Features a dynamic gradient (purple to yellow) with adjustable intensity based on OI.
Note: Exact trader leverage isn’t known; liquidation zones are inferred from market psychology, as traders often favor specific leverage levels (e.g., 25x, 50x, 125x).
Histograms
Display the density of potential liquidity across price levels, complementing the Heatmap. Note that the largest histogram bars may appear in different locations compared to the most intense (yellow) areas of the Heatmap, as histograms primarily focus on the accumulation of smaller orders.
OI Table
Aggregates OI data from all supported exchanges (Binance, BitMEX, OKX, Kraken) in base currency and USD, sortable by volume.
Displays total OI and individual exchange contributions automatically.
Liquidity Exit Bubbles
Plots bubbles for significant negative OI changes, sized as small, medium, or large based on magnitude.
Positioned above or below candles depending on volatility direction, with customizable colors.
Volume Bubbles
Marks high-volume activity from perpetual futures, with sizes (normal, high, ultra-high) tied to intensity.
Offers adjustable sensitivity and offset for precise placement.
RSI Labels
Provides real-time RSI readings, highlighting overbought (≥70) and oversold (≤30) levels.
Configurable by price source (e.g., High/Low, Close) and timeframe, with customizable appearance.
Moving Averages
Supports SMA, EMA, WMA, and VWMA with three user-defined periods (default: 21, 50, 100).
Toggleable visibility and colors for trend analysis.
How to Use
Scalping/Day Trading (1m-15m):
Load the indicator three times: one at 125x leverage (visible), one at 50x (hidden), and one at 25x (hidden). Use the 125x Heatmap to identify immediate liquidation zones. When price breaks through the 125x liquidity pool, enable the 50x instance, then 25x as needed, to track cascading liquidations.
Pair with Histograms to monitor potential liquidity density, Volume Bubbles for breakout signals, and Liquidity Exit Bubbles for reversals.
Check RSI Labels on short timeframes (e.g., 15m) for overextended moves.
Swing Trading (1H-4H):
Set the Heatmap to lower leverage (e.g., 25x, 10x) and combine with Moving Averages to confirm trends.
Use RSI Labels on matching timeframes to time entries/exits based on momentum.
Reference the OI Table to assess overall market exposure.
Liquidity Analysis:
Adjust the Minimum Liquidity Sensitivity to focus on significant OI clusters. Higher filtering removes small orders, so use Volume Bubbles and the OI Table for broader context in sideways markets.
Use the OI Table to see total OI across all exchanges.
General Tips:
Toggle features (e.g., Bubbles, MAs) to focus on relevant data.
Test settings on your asset—optimized for Bitcoin, adjustable for altcoins.
Settings
Exchanges: Data from Binance, BitMEX, OKX, and Kraken is automatically included.
Heatmap: Enable Longs/Shorts, set start date, adjust leverage and color intensity.
Liquidity Filtering: Tune Minimum Liquidity Sensitivity (0.1-1.0) to balance detail and noise.
Histograms: Automatically active, showing potential liquidity density; no direct settings.
OI Table: Toggle visibility and choose position (e.g., Top Right).
Bubbles: Enable/disable Liquidity Exit and Volume Bubbles, set sensitivities and colors.
RSI: Pick price source, timeframe, and label style (size, color, offset).
Moving Averages: Select type, periods, and visibility.
Why It’s Unique
This indicator blends liquidity tools (Heatmap, Histograms, OI Table, Bubbles) with momentum and trend analysis (RSI, MAs). The adjustable Heatmap intensity enhances visibility of significant OI levels, while the multi-tool approach provides a fuller market perspective.
Notes
Best suited for perpetual futures; test on spot or other instruments for compatibility.
High leverage (e.g., 125x) excels on short timeframes; use 5x-25x for daily/weekly views.
Experiment with settings to optimize for your asset and timeframe.
This indicator relies on the availability of Open Interest (OI) data from TradingView. Functionality may vary depending on data access for your chosen asset and exchange.
Feedback
Your input is valued to enhance this tool. Enjoy trading with a fuller market perspective!
HTF Candle Volume Thermometer [ChartPrime]The HTF Candle Volume Thermometer is a powerful volume heatmap tool that visualizes higher timeframe candle volume distributions directly on the chart. It helps traders identify key price levels where liquidity is concentrated, allowing for more informed trading decisions.
⯁ KEY FEATURES
Higher Timeframe Volume Mapping
Uses higher timeframe (HTF) candles to create a heatmap of volume distribution within each candle.
Dynamic Volume Heatmap
Colors each HTF candle background green for bullish and red for bearish, with a gradient heat overlay highlighting volume concentration.
Max Volume Point Identification
Marks the level within each HTF candle where the highest volume was recorded, using red for the most significant volume area.
Fully Customizable Display
Users can adjust the HTF timeframe, color settings, and resolution to tailor the indicator to their trading preferences.
Segmented Volume Distribution
Each HTF candle is divided into smaller levels, allowing traders to see volume changes within the range of each candle.
Key Level Detection
Max volume points often act as key support and resistance levels where price is likely to react, helping traders refine their strategies.
⯁ HOW TO USE
Identify Liquidity Zones
Use the max volume levels to determine areas where price is likely to find support or resistance.
Assess Trend Strength
Compare volume distribution between bullish and bearish HTF candles to gauge market momentum.
Optimize Trade Entries & Exits
Look for price reactions at high-volume areas to refine stop-loss and take-profit levels.
Adjust Heatmap Resolution
Customize the resolution setting to get a more detailed or broader view of volume segmentation within HTF candles.
⯁ CONCLUSION
The HTF Candle Volume Thermometer is a must-have tool for traders who want to integrate volume analysis with higher timeframe structures. By visualizing volume heatmaps within each HTF candle, this indicator helps traders pinpoint critical liquidity zones and key price levels.
Heatmap Suite [PhenLabs]📊 Heatmap Suite
Version: PineScript™ v6
📌 Description
The Heatmap Suite is an advanced technical analysis tool that combines multiple density calculation methods with dynamic visualization to identify significant price levels and trading activity zones. It features a sophisticated analysis system that processes price and volume data through various kernel methods, providing traders with insights into market structure, support/resistance zones, and potential price reaction areas.
🚀 Points of Innovation:
Multi-method density calculation incorporating three distinct approaches
Adaptive visualization system with dynamic color gradients
Real-time dashboard with key market metrics
Significant level detection with automatic threshold adjustment
🚨 Important🚨
🔸Comprehensive tooltips included in the PhenLabs dashboard for in depth guidance
🔧 Core Components
Density Analysis: Multiple calculation methods for price distribution assessment
Heat Mapping: Dynamic visualization of price congestion zones
Level Detection: Automatic identification of significant price levels
Dashboard System: Real-time market metrics and analysis
🔥 Key Features
The indicator provides comprehensive analysis through:
Kernel Density: Traditional balanced view of price distribution
Exponential Kernel: Time-weighted analysis emphasizing recent price action
Volume-Weighted: Focus on high-volume price areas
Significant Levels: Automatic detection of important price zones
Heat Distribution: Color-coded visualization of price congestion
🎨 Visualization
Heat Zones: Shows intensity of price activity
Significant Lines: Key level indicators
Color Gradients: Indicates density strength
Dashboard Display: Real-time metrics
Dynamic Opacity: Reflects density intensity
📖 Usage Guidelines
The indicator offers several customization options:
Basic Settings:
Calculation Method: Choose between three density calculation approaches
Lookback Period: Analysis timeframe adjustment
Zone Count: Price range division granularity
Heat Sensitivity: Contrast adjustment for visualization
🎛️ Visual Settings:
Dashboard Size: Text size customization
Position: Dashboard placement options
Color Scheme: Heat map gradient visualization
Level Display: Significant price zone indicators
✅ Best Use Cases:
Identify strong support/resistance zones through high-density areas
Spot potential price reversal zones at significant levels
Analyze price congestion patterns
Monitor real-time changes in market structure
⚠️ Limitations
Requires sufficient historical data
Computational intensity increases with longer lookback periods
Heat sensitivity needs adjustment based on market conditions
Dashboard placement may need adjustment based on price action
💡 What Makes This Unique
Multi-method Analysis: Three distinct calculation approaches
Adaptive Visualization: Dynamic color gradient system
Real-time Metrics: Comprehensive dashboard display
Automatic Level Detection: Significant price zone identification
Memory-efficient Design: Optimized calculation methods
🔬 How It Works
The indicator processes market data through four main components:
1. Density Calculation:
Processes price and volume data
Applies selected kernel method
Generates density distribution
2. Heat Mapping:
Converts density values to color gradients
Updates visualization in real-time
Displays price congestion zones
3. Level Detection:
Identifies significant price levels
Applies threshold filtering
Marks important zones
4. Dashboard Updates:
Calculates real-time metrics
Updates display components
Provides market context
💡Note:
The indicator performs best with adequate historical data and proper sensitivity settings. Its sophisticated density analysis provides valuable insights into market structure beyond traditional support/resistance indicators.
WhaleTrackBITGET:BTCUSDT.P
WhaleTrack – Volume Heatmap to Uncover Institutional Trading Activity
Overview
WhaleTrack is a volume-based heatmap indicator designed to reveal areas of high institutional trading activity. The indicator helps traders identify hidden support and resistance levels, analyze trend sustainability, and optimize stop-loss placements by displaying where significant market participants (whales) have historically traded in large volumes.
Institutions and large traders often push price into areas of historical liquidity to trigger retail stop-losses and fill their own large orders at optimal prices. WhaleTrack visualizes these critical areas, allowing traders to anticipate future price movements based on past institutional behavior.
How WhaleTrack Works
WhaleTrack analyzes historical trading volume and calculates a normalized volume intensity relative to the moving average (SMA). This data is then mapped onto a heatmap that highlights key liquidity zones.
1. Volume Normalization & SMA-Based Calculation
The script calculates the ratio of current volume to its SMA-based average.
Zones with significantly high volume spikes are identified as key liquidity areas where large traders may have accumulated or distributed assets.
The volume is quantized into different levels, ranging from Low to Extreme, creating a clear heatmap gradient.
2. Why Do Whales Manipulate Liquidity?
Large traders (whales) need liquidity to execute their orders.
They push price into historical high-volume areas to trigger stop-losses and force retail traders into selling.
This behavior allows them to accumulate at lower prices or distribute at higher prices before a major move.
Whale zones often act as support/resistance because institutions tend to protect their previous accumulation or distribution levels.
3. Heatmap Color Model & Zone Classification
WhaleTrack assigns volume intensity levels based on historical market participation:
Low → Minimal volume, weak interest
Low-Mid → Slightly increased volume
Mid → Standard trading activity, no major anomalies
Mid-High → Significant increase in volume, possible whale activity
High → Strong liquidity pool, institutional interest
Extreme → Highly concentrated volume, key reversal area
By observing these zones, traders can determine whether a price level is likely to hold as support or resistance , or if a breakout has the strength to sustain.
Trading Applications of WhaleTrack
WhaleTrack can be used to identify trade setups based on liquidity behavior:
1. Identifying Hidden Reversal Points (Support & Resistance)
Large Whale Zones below price → Likely strong support.
Large Whale Zones above price → Likely strong resistance.
These zones often lead to reversals, as large traders defend their previous positions.
2. Evaluating Trend Sustainability
A strong uptrend should leave multiple high-volume zones behind.
If no new high-volume zones form, the trend may be unsustainable.
High volume clusters in trend direction? → Likely trend continuation.
3. Optimizing Stop-Loss Placement
Placing stops inside whale zones increases stop-out risk.
Setting stops below whale buy zones protects against premature liquidation.
Stops above whale sell zones help avoid fake breakouts.
Customization & Settings
WhaleTrack is designed with flexibility in mind, offering multiple customization options:
1. Layout & Color Models
WhaleTrack Default – optimized for whale volume tracking
Model 1 & Model 2 – alternative heatmap color schemes
Contrast Mode – high visibility
White-Black & Black-White – for different chart backgrounds
Custom 1 & Custom 2 – user-defined color configurations
2. Advanced Options
Draw Full Candle Boxes – display full candle height or a partial range
Legend Visibility & Positioning – control placement of the heatmap legend
Exponential Color Model – choose between logarithmic and linear volume representation
Max Transparency Settings – adjust visibility of older zones
Number of Heatmap Colors – set the gradient sensitivity
3. Data Optimization Settings
Lookback Period – define how many bars are analyzed for volume normalization
Max Box Display – limit the number of displayed volume zones
Data Saver Mode – increase range at the expense of detail
Minimum Volume Threshold – filter out insignificant volume clusters
Disclaimer
This indicator is for educational and informational purposes only. It does not provide financial advice or guarantee future performance. Trading is risky—conduct your own research before making any investment decisions.
Dynamic Deviation Levels [BigBeluga]Dynamic Deviation Levels is an innovative indicator designed to analyze price deviations relative to a smoothed midline. It provides traders with visual cues for overbought/oversold zones, price momentum, levels through labeled deviations and gradient candle coloring.
🔵Key Features:
Smoothed Midline:
A central line calculated as a smoothed median of the price source, serving as the baseline for price deviation analysis.
Dynamic Deviation Levels:
- Three deviation levels are plotted above and below the midline, with labels (1, 2, 3, -1, -2, -3) marking significant price movements.
- Helps traders identify overbought and oversold market conditions.
Heat-Colored Candles:
- Candle colors shift in intensity based on the deviation level, with four gradient shades for both upward and downward movements.
- Quickly highlights market extremes or stable zones.
Interactive Color Scale:
- A gradient scale at the bottom right of the chart visually represents deviation values.
- A triangle marker indicates the current price deviation in real time.
Optional Deviation Levels Display:
- Traders can enable all dynamic levels on the chart to visualize support and resistance areas dynamically.
🔵Usage and Benefits:
Identify Overbought/Oversold Zones: Use labeled deviation levels and heat-colored candles to spot stretched market conditions.
Track Trend Reversals and Momentum: Monitor price interactions with deviation levels for potential trend continuation or reversal signals.
Real-Time Deviation Insights: Leverage the color scale and triangle marker for live deviation tracking and actionable insights.
Map Dynamic Support and Resistance: Enable dynamic levels to highlight key areas where price reactions are likely to occur.
Dynamic Deviation Levels is an indispensable tool for traders aiming to combine price dynamics, momentum analysis, and visual clarity in their trading strategies.
[COG]MTF RZP Heatmap MTF RZP Heatmap (Range Zone Pulse)
What It Does
This indicator creates three visual heatmaps that show how current price movement compares to the average range of different timeframes. It helps traders:
Identify when price moves are overextended
Compare momentum across different timeframes
Spot potential reversal points
Understand the relative strength of price movements
How It Works
Range Calculation:
For each selected timeframe, it calculates an average range based on the specified number of periods
The range is measured from high to low for each period
A moving average of these ranges creates a dynamic "normal" range for that timeframe
Position Calculation:
Measures how far price has moved from the period's opening price
Compares this movement to the average range
Converts the movement into a percentage (-100% to +100%)
Visual Display:
Shows three vertical heatmaps, one for each timeframe
Colors graduate from bearish (typically red) to bullish (typically green)
A dot indicator shows the current position within each range
Percentage labels show exact movement relative to average range
Trading Applications
Trend Trading:
Multiple timeframes aligned in the same color suggest strong trend
Use larger timeframes (Daily/Weekly) for trend direction
Use smaller timeframes (4H/1H) for entry timing
Mean Reversion:
Extreme readings (near +100% or -100%) suggest overextended moves
Look for divergences between timeframes
Use when shorter timeframes show extremes but larger timeframes don't
Volatility Trading:
Compare current moves to average ranges
Identify when markets are more volatile than usual
Adjust position sizes based on range expansion/contraction
Multi-Timeframe Analysis:
Compare price action across different time horizons
Identify conflicting signals between timeframes
Use for timeframe alignment in trading decisions
Best Practices for Usage
Timeframe Selection:
Set the first timeframe to your trading timeframe
Set the second timeframe to your trend timeframe
Set the third timeframe to your entry timeframe
Range Period Settings:
Default is 5 periods
Increase for more stable readings
Decrease for more responsive readings
Color Interpretation:
Darker colors indicate stronger moves
Look for alignment across timeframes
Watch for extremes in any timeframe
Trading Setups:
Wait for alignment in multiple timeframes
Use extreme readings for counter-trend trades
Combine with other indicators for confirmation
