POC Sweep Reclaim [LuxAlgo]The POC Sweep Reclaim (PSR) model identifies a two-step "rejection then acceptance" price action pattern centered around the Point of Control (POC) of previous candles. By approximating volume-at-price data using lower timeframe (LTF) granularity, the tool highlights specific liquidity traps where price first fails to sustain a move beyond a high-volume level and subsequently reclaims it.
The PSR framework is built on the logic that a "Sweep" represents a failed probe of value, while the "Reclaim" represents a successful breach, signaling a potential shift in market dominance as price moves away from trapped participants.
🔶 USAGE
The indicator visualizes market microstructure dynamics through a sequence of two distinct events:
🔹 The Sweep (The Rejection)
A sweep occurs when a candle's wick trades through the previous bar's POC, but the candle body fails to close beyond it. This identifies a "Liquidity Grab" where price interacts with a high-volume node but fails to find acceptance, often trapping breakout traders.
Buyside Sweep: Price wicks above the previous POC but closes below it (Bearish Rejection). Sellside Sweep: Price wicks below the previous POC but closes above it (Bullish Rejection).
🔹 The Reclaim (The Acceptance)
A reclaim occurs when the candle immediately following a sweep successfully closes beyond the same POC level that was just rejected.
BSR (Buyside Reclaim): A bullish signal where price closes above a previously swept upper POC, suggesting the trap is resolved to the upside. SSR (Sellside Reclaim): A bearish signal where price closes below a previously swept lower POC, suggesting follow-through to the downside.
🔶 DETAILS
The script aims to bridge the gap between standard OHLCV analysis and order-flow dynamics. While a true footprint engine (available on higher PulseWire tiers) is more accurate, this script uses a proxy by aggregating volume from a lower timeframe (e.g., 1-minute) to estimate the POC of higher-timeframe bars.
🔹 Academic Intuition
Order-Flow Imbalance (OFI): Short-term price changes are strongly linked to the inability of one side to provide enough depth. A "reclaim" reflects a shift where the dominant side successfully absorbs the liquidity that caused the initial rejection. Salient Prices: High-volume nodes like the POC act as psychological and mechanical barriers. Research indicates that liquidity clusters around these prominent prices, making them significant areas for support/resistance. Stop-Loss Cascades: Sweeps often interact with clustered stop-loss orders. If price reclaims the level after clearing these stops, it can trigger a directional move as the market "clears" the liquidity hurdle.
🔹 Practical Limitations
Footprint Proxy: The POC is calculated by aggregating volume at the close of LTF bars. This is a noisy proxy compared to a true footprint, which tracks every tick. Data Snooping: Like all pattern-based indicators, the "Reclaim" logic should be verified with robust backtesting to ensure signals are not the result of random price noise. Repainting: Because the POC depends on LTF data, the values for the current developing bar may fluctuate until the candle closes.
🔶 SETTINGS
Lower Timeframe for POC: Sets the granularity for volume aggregation. A lower value (like 1m) provides a more precise POC proxy. Show POC: Toggles the visibility of the calculated Point of Control dots for every bar. Show Sweep Dots: Displays markers at the POC level when a wick interaction occurs without a body close. Show BSR (Buyside Reclaim): Highlights candles that successfully close above a swept buyside POC. Show SSR (Sellside Reclaim): Highlights candles that successfully close below a swept sellside POC. Indicator
KDE Value Clouds [LuxAlgo]The KDE Value Clouds indicator is a quantitative tool that uses Kernel Density Estimation (KDE) to visualize the statistical distribution of price action, identifying high-density "Value Clouds" where the market has spent the most time.
🔶 USAGE
The indicator highlights areas of price " fair value " by calculating the probability density of price across a user-defined lookback period. Traders can use these density clusters to identify significant support and resistance levels that are often invisible to standard trend-following indicators.
🔹 Value Clouds
The " Value Clouds " appear directly on the price chart as gradient boxes. These clouds highlight regions where the density of price action exceeds the 50th percentile of the total distribution.
High Density (Bright Colors): Indicates a "Balance Area" where the market has reached a temporary equilibrium. These often act as magnets for price.
Low Density (Gaps): Indicates "Inefficiency" or fast moves where the market did not spend much time. These areas are often revisited or "filled" later.
🔹 KDE Profile & POC
On the right side of the chart, a smooth horizontal profile represents the continuous density function. The KDE POC (Point of Control) is the single price level with the highest calculated density within the lookback period, serving as the ultimate "anchor" for the current market regime.
🔹 How to use
Traders can look for price to "stall" or range within the bright Value Clouds, as these represent accepted price levels. When price moves into a "Gap" (a low-density area), it often moves quickly until it reaches the next cloud.
The KDE POC can be used as a primary support or resistance level; a breakout above a high-density cloud often signals a shift in market sentiment, while a rejection at the edge of a cloud suggests the market is still in a balanced state.
🔶 DETAILS
🔹 KDE vs. Volume Profile
A standard Volume Profile relies on "bins" (rectangles) to count volume at specific price steps. This can create "jagged" profiles that change drastically depending on the chosen row size.
The KDE Value Clouds approach is different because it uses a continuous probability function. Every price point in the lookback period contributes a small "bell curve" of influence to the total profile. This allows for a much smoother and more mathematically sound representation of where " Value " actually resides, regardless of arbitrary bin sizes.
The core of this indicator relies on two primary mathematical concepts:
Gaussian Kernel Estimation: Instead of simply counting occurrences, the script applies a Gaussian weight to every price point. This results in a "smooth" profile that captures the true shape of the price distribution.
Silverman’s Rule of Thumb: To prevent the clouds from being too noisy or too blurry, the indicator uses Silverman’s rule to calculate an optimal " Bandwidth ." This bandwidth adapts based on the standard deviation of the price data, ensuring the visualization stays relevant across different volatility regimes.
🔶 SETTINGS
🔹 Main Settings
Lookback Period: The number of bars used to calculate the price density. A higher lookback provides a "macro" view of value, while a lower lookback focuses on recent rotations.
Bandwidth Multiplier: Adjusts the "smoothness" of the KDE curve. Increasing this value will make the clouds broader and smoother; decreasing it will make them more granular.
Precision (Steps): Defines the vertical resolution of the density calculation. Higher values result in a more detailed profile.
🔹 Visualization
High/Low Density Colors: Customizes the gradient used for both the side profile and the on-chart clouds.
Profile Width (%): Controls how far the KDE profile extends horizontally across the right side of the chart.
Show Value Cloud on Chart: Toggles the visibility of the background "clouds" that highlight high-density price zones.
Indicator
Anchored Clusters Volume Profile [LuxAlgo]The Anchored Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups within a user-defined range and generates individual volume profiles for each detected cluster. This tool provides a unique perspective on volume distribution by isolating price behaviors based on proximity rather than strictly chronological order.
🔶 USAGE
The indicator identifies "clusters" of price activity within a selected range defined by a starting and ending date. Each cluster is assigned a unique color and its own horizontal volume profile, allowing traders to see where liquidity is most concentrated within specific price regimes.
🔹 Identifying Institutional Zones
Traders can use the Point of Control (POC) of high-volume clusters to identify significant institutional interest. Because the K-Means algorithm groups price action by density rather than time, a cluster's POC often represents a "fair value" level where significant exchange occurred. These dashed POC lines frequently act as robust support or resistance levels when price returns to them in the future.
🔹 Market Regime Detection
By observing the vertical distribution and overlap of clusters, traders can identify market phases. Overlapping clusters with high volume often indicate accumulation or distribution phases (sideways markets), whereas distinct, vertically separated clusters with lower volume gaps between them suggest a trending environment. A shift from multiple overlapping clusters to a new, isolated cluster can signal a breakout or the start of a new trend.
🔹 Precision Entry & Exits
Cluster boundaries and POC lines provide concrete levels for trade management. An entry can be sought when price retests a high-volume cluster POC, while stops can be placed outside the total price range of that specific cluster (the area covered by its volume profile). Conversely, targets can be set at the POC of the next major cluster above or below current price action.
🔹 Volume Conviction
The tool provides specific volume metrics that allow traders to gauge conviction. By comparing the "Total" volume label of one cluster against another, a trader can determine which price regime had more participation. A breakout into a price zone with a high-volume cluster suggests stronger conviction and a higher probability of the level holding compared to a zone with low total volume.
🔶 DETAILS
Unlike traditional anchored volume profiles that provide a single histogram, this script employs a K-Means clustering algorithm to segment the range. This process involves:
Identifying the specific range of bars between the user-selected Start Time and End Time .
Initializing "centroids" across the price range of that period.
Iteratively assigning each price bar to the nearest centroid based on the HLC2 (median) price.
Recalculating centroids based on the volume-weighted average price of the assigned bars.
Finalizing assignments after the specified number of iterations to ensure stable clusters.
By separating price action into these clusters, the tool helps identify high-interest zones that might be obscured by a single, traditional Volume Profile.
🔶 SETTINGS
🔹 Anchor Settings
Start Time / End Time : Sets the beginning and end of the analysis range. These use the "Confirm" feature, allowing you to select the range directly on the chart after adding the indicator or changing settings.
Range Highlight : Adjusts the color and transparency of the background shading that identifies the analyzed range.
🔹 Clustering Settings
Number of Clusters : Sets how many distinct price groups the algorithm should attempt to find (2 to 10).
K-Means Iterations : Controls the number of times the algorithm refines the cluster centers. Higher values can lead to more stable results.
🔹 Volume Profile Settings
Rows per Cluster VP : Defines the vertical resolution (number of bins) for each individual cluster's profile.
Max VP Width (Bars) : Sets the maximum horizontal length of the volume profile histograms.
VP Offset : Adjusts the horizontal spacing between the current bar and the start of the volume profiles.
Highlight Price Dots : Toggles the visibility of the colored dots on the price action to identify cluster assignments.
Dot Size : Adjusts the size of the cluster assignment dots on the chart, ranging from tiny to huge.
Indicator
Indicator
Key Price Levels Daily Ver [structJCWL]As an independent developer and active trader, I built this tool to address a personal frustration: the lack of precision in standard session-level indicators. For those who trade based on market micro-structure, a level that is "approximately" correct is a logic error.
Key Price Levels Daily Ver is an engineering implementation focused on reconstructing session data with surgical accuracy. It is designed for traders who require reliable, noise-filtered benchmarks to anchor their daily execution.
THE ENGINEERING LOGIC:
1-Second (1S) Intrabar Reconstruction : Standard scripts often rely on daily-bar data, which can be imprecise. This tool re-aggregates the session using 1-Second (1S) granularity . By processing every sub-candle, it ensures that POC, Value Area, and Extremes are anchored to the true weight of capital commitment.
The 15-Minute Threshold (Technical Rationale) : To maintain the high fidelity of 1S data aggregation, this indicator is optimized for timeframes ≤ 15 minutes . Calculating micro-structure on higher timeframes leads to data thinning and reduced precision—limitations I refuse to accept in my personal trading setup.
Adaptive Visual Hierarchy (Fade & Limits) : To prevent visual clutter, I’ve integrated Fade Strength and Timeframe Limits . Lines automatically adjust their transparency or visibility based on your current timeframe, ensuring that the most relevant levels remain clear while secondary noise recedes.
Reliability by Design : In 24/7 markets, script stability is paramount. I have implemented defensive programming (including a 98,000-unit memory circuit breaker ) to prevent overflows during extreme volatility. I strive for absolute dependability; if you encounter any issues, your feedback is highly welcome as we refine and improve this tool together.
作为一名独立开发者与实战交易者，我构建此工具是为了解决一个长期的困扰：标准时段指标精度的缺失。对于基于市场微观结构进行交易的人来说，“大约准确”的水位本身就是一种逻辑错误。
Key Price Levels Daily Ver 是我对日内关键水位线的个人工程化实现，专注于以“外科手术式”的精度重构时段数据。它专为那些需要可靠、过滤噪音的基准位来锚定日内执行的交易者而设计。
核心设计逻辑：
1秒级微观重构 (1S Precision) ：我不满足于直接调用现成的日线数据。脚本通过扫描 1秒 (1S) 微观颗粒度数据 来重构整个交易时段，确保 POC 和价值区的计算基准来自于最真实的成交细节。
15分钟周期硬限制 ：为了保证 1S 数据聚合的极致精度，本脚本仅限在 15分钟及以下周期 运行。在更长周期调用微观数据会导致精度大幅下降。作为开发者，我不允许“大约准确”的逻辑存在于我的系统里。
自适应视觉层级 (Fade & Limits) ：通过内置的 淡化强度 (Fade) 与 周期限制 (Limits) ，水位线会根据你当前的图表周期自动调节透明度或可见性。
可靠性设计 ：在 24/7 不间断的交易中，脚本的稳定性至关重要。我在代码底层加入了防御性设计（包括 98,000 数组容量熔断机制 ），旨在防止极端行情下的计算溢出或图表崩溃。我追求的是一个能让人放心的工具，若你在使用中发现任何问题，欢迎随时反馈，我们共同完善。
Feedback & Interaction:
As a personal project, this script is continuously refined. I welcome any bug reports or suggestions for improvement to make this tool even more robust for the community.
Compliance & Credits:
Author: structJCWL (Independent Developer & Trader)
License: Mozilla Public License 2.0 (MPL 2.0)
Attribution: Core data aggregation and state-machine logic are 100% original implementations Indicator
Liquidity Depth Indicator [BigBeluga]🔵 OVERVIEW
Liquidity Depth Indicator visualizes the distribution, balance, and depth of liquidity within a selected lookback window.
It builds a full liquidity profile by scanning how often price interacted with each price level, generating cumulative buy-side and sell-side curves, heatmap intensity zones, and a clear Point of Control (PoC).
This provides a deep structural view of where liquidity sits, how it’s stacked, and which side of the market dominates.
🔵 CONCEPTS
Liquidity Binning — The full range (High → Low) over the lookback window is divided into 100 micro-price zones. Each candle contributes its volume into the bin closest to its close.
Buy/Sell Side Split —
• Zones above the midpoint = potential sell-side liquidity concentration.
• Zones below midpoint = potential buy-side liquidity concentration.
Cumulative Liquidity Curves — Each side builds a flowing curve showing how liquidity stacks from mid toward extremes.
Liquidity Heatmap — Highlights volume density at each price level using color intensity for easy visual analysis.
Point of Control — The price level with the highest liquidity accumulation (max volume bin).
🔵 FEATURES
100-Level Volume Distribution — Scans all candles inside the lookback period and assigns each close to the nearest bin.
Sell-Side Depth Curve — Plotted above the midpoint, showing how sell liquidity increases as price moves toward the top of the range.
Buy-Side Depth Curve — Plotted below the midpoint, showing how buy liquidity builds toward the bottom of the range.
Volume Labels — Displays total buy-side and sell-side volume at curve peaks.
Mid-Range Liquidity Split — Calculates:
• Buy-side liquidity %
• Sell-side liquidity %
Displayed in two vertical boxes next to the profile.
Liquidity Heatmap Overlay — Color-coded price strips showing where largest clusters sit:
• Above midpoint → Sell zones
• Below midpoint → Buy zones
PoC Detection — Draws the strongest liquidity level with a bold line and prints its relative volume.
Range Frame Lines — High, Low, and Mid lines are plotted to define the liquidity environment.
Auto-Cleanup & Rebuild — All curves, boxes, and heatmap segments are refreshed each bar using barstate.islast.
🔵 HOW IT WORKS
1. Determines High, Low, and Mid over the lookback window.
2. Divides the range into 100 bins based on linear spacing.
3. Accumulates volume per bin depending on where each candle’s close lands.
4. Builds total, buy-side, and sell-side cumulative curves .
5. Colors the heatmap based on normalized volume per bin.
6. Locates PoC — the bin with maximum volume.
7. Creates buy/sell percentage distribution boxes .
🔵 HOW TO USE
Identify Liquidity Imbalance — Compare the buy vs sell percentage boxes to see which side dominates.
Spot Strong Liquidity Walls — Thick portions of cumulative curves represent areas price may reject.
Read PoC as a Magnet — Price often gravitates toward the PoC or reacts strongly once reached.
Evaluate Breakout Strength — If liquidity is concentrated at extremes, a breakout may face strong absorption.
Use Heatmap as Hidden S/R — The brightest zones frequently act as hidden support/resistance.
Detect Exhaustion Areas — If a curve sharply thins near highs or lows, the trend may weaken.
🔵 CONCLUSION
Liquidity Depth Indicator is a powerful microstructure tool that reveals where liquidity is stacked within a price range.
By combining cumulative curves, heat mapping, and PoC identification, it exposes both visible and hidden liquidity layers — helping traders spot absorption zones, reversal clusters, liquidity pools, and trend exhaustion areas with high clarity. Indicator
Precision Market Entropy Heatmap [LuxAlgo]The Precision Market Entropy Heatmap indicator provides a high-resolution visualization of volume distribution and market activity within specific anchor intervals using intrabar data.
By utilizing lower timeframe (LTF) precision, it maps out where the most significant trading activity occurred, allowing traders to identify institutional interest zones and "fair value" areas through a dynamic heat-mapped profile.
🔶 USAGE
The indicator segments the chart into blocks based on the selected Anchor Interval. Within each block, a vertical distribution of volume is calculated using the Intrabar Precision setting to ensure the heatmap accurately reflects market participation at specific price levels.
Heatmap Blocks : Brighter colors represent higher volume concentrations (high entropy). These areas often act as significant support or resistance zones where the market has previously found "fair value" or high liquidity.
Identifying Institutional Interest : High-volume "bright" nodes represent price levels where heavy institutional participation occurred. These nodes act as powerful magnets or barriers for future price action.
Navigating Liquidity Voids : Darker areas indicate low volume nodes (low entropy). Price often "slips" through these gaps quickly. Traders can use these zones to anticipate fast-moving price action or set targets beyond the void.
Trend Direction via POC : Observe the slope and shifts of the Developing POC polyline. An ascending POC confirms bullish value migration, while a descending one suggests bearish value migration.
Mean Reversion : Significant price deviations from the largest high-volume node, when the POC remains static, can signal that the market is overextended and likely to return to "fair value."
Breakout Validation : Use the blocks to identify compression zones. A breakout is more reliable when the POC shifts into the new range, confirming that the move is backed by volume and accepted by the market.
POC Extensions : Dashed lines extend the session's final POC. These are dynamically colored based on their relationship to the current price: Green if the POC is below the current price (potential support) and Red if above (potential resistance).
🔶 DETAILS
Unlike standard Volume Profiles that look at fixed ranges, this script focuses on "Entropy" by visualizing the density of distribution across a user-defined grid.
By requesting security data from lower timeframes, it provides a much more granular view of price action than what is visible on the current chart timeframe alone.
The indicator uses a gradient-based coloring system to distinguish between low-activity areas and high-volume nodes, making it easier to spot "Liquidity Voids" (darker areas) and "High Volume Nodes" (brighter areas).
🔶 SETTINGS
🔹 Heatmap Settings
Anchor Interval : Sets the timeframe that defines each heatmap block (e.g., "D" for Daily blocks).
Intrabar Precision : Determines the lower timeframe used to calculate the volume distribution. Lower values (like "1m") provide higher precision but are limited by available historical data.
Number of Rows : Controls the vertical price resolution of the heatmap grid. Higher values create a more detailed but computationally heavier profile.
🔹 Style Settings
Heatmap Intensity : A three-color gradient selector that defines the color transition from low to high volume areas.
Heatmap Transparency : Adjusts the visibility of the heatmap blocks on the chart.
POC Extension (Bull/Bear) : Sets the colors for the dashed POC lines based on whether they are currently below (Bull) or above (Bear) the market price.
Show Developing POC : Toggles the visibility of the real-time POC polyline.
Auto : When enabled, the developing POC color automatically syncs with your chart theme's foreground color.
🔹 Display Settings
Max Sessions to Show : Limits the number of historical heatmap blocks rendered on the chart to maintain performance.
Extend POCs to Current Bar : When enabled, historical POC lines will extend to the far right of the chart until they are replaced by newer sessions.
Indicator
Volume Profile Skew [BackQuant]Volume Profile Skew
Overview
Volume Profile Skew is a market-structure indicator that answers a specific question most volume profiles do not:
“Is volume concentrating toward lower prices (accumulation) or higher prices (distribution) inside the current profile range?”
A standard volume profile shows where volume traded, but it does not quantify the shape of that distribution in a single number. This script builds a volume profile over a rolling lookback window, extracts the key profile levels (POC, VAH, VAL, and a volume-weighted mean), then computes the skewness of the volume distribution across price bins. That skewness becomes an oscillator, smoothed into a regime signal and paired with visual profile plotting, key level lines, and historical POC tracking.
This gives you two layers at once:
A full profile and its important levels (where volume is).
A skew metric (how volume is leaning within that range).
What this indicator is based on
The foundation comes from classical “volume at price” concepts used in Market Profile and Volume Profile analysis:
POC (Point of Control): the price level with the highest traded volume.
Value Area (VAH/VAL): the zone containing the bulk of activity, commonly 70% of total volume.
Volume-weighted mean (VWMP in this script): the average price weighted by volume, a “center of mass” for traded activity.
Where this indicator extends the idea is by treating the volume profile as a statistical distribution across price. Once you treat “volume by price bin” as a probability distribution (weights sum to 1), you can compute distribution moments:
Mean: where the mass is centered.
Standard deviation: how spread-out it is.
Skewness: whether the distribution has a heavier tail toward higher or lower prices.
This is not a gimmick. Skewness is a standard statistic in probability theory. Here it is applied to “volume concentration across price”, not to returns.
Core concept: what “skew” means in a volume profile
Imagine a profile range from Low to High, split into bins. Each bin has some volume. You can get these shapes:
Balanced profile: volume is fairly symmetric around the mean, skew near 0.
Bottom-heavy profile: more volume at lower prices, with a tail toward higher prices, skew tends to be positive.
Top-heavy profile: more volume at higher prices, with a tail toward lower prices, skew tends to be negative.
In this script:
Positive skew is labeled as ACCUMULATION.
Negative skew is labeled as DISTRIBUTION.
Near-zero skew is NEUTRAL.
Important: accumulation here does not mean “buying will immediately pump price.” It means the profile shape suggests more participation at lower prices inside the current lookback range. Distribution means participation is heavier at higher prices.
How the volume profile is built
1) Define the analysis window
The profile is computed on a rolling window:
Lookback Period: number of bars included (capped by available history).
Profile Resolution (bins): number of price bins used to discretize the high-low range.
The script finds the highest high and lowest low in the lookback window to define the price range:
rangeHigh = highest high in window
rangeLow = lowest low in window
binSize = (rangeHigh - rangeLow) / bins
2) Create bin midpoints
Each bin gets a midpoint “price” used for calculations:
price = rangeLow + binSize * (b + 0.5)
These midpoints are what the mean, variance, and skewness are computed on.
3) Distribute each candle’s volume into bins
This is a key implementation detail. Real volume profiles require tick-level data, but Pine does not provide that. So the script approximates volume-at-price using candle ranges:
For each bar in the lookback:
Determine which bins its low-to-high range touches.
Split that candle’s total volume evenly across the touched bins.
So if a candle spans 6 bins, each bin gets volume/6 from that bar. This is a practical, consistent approximation for “where trading could have occurred” inside the bar.
This approach has tradeoffs:
It does not know where within the candle the volume truly traded.
It assumes uniform distribution across the candle range.
It becomes more meaningful with larger samples (bigger lookback) and/or higher timeframes.
But it is still useful because the purpose here is the shape of the distribution across the whole window, not exact microstructure.
Key profile levels: POC, VAH, VAL, VWMP
POC (Point of Control)
POC is found by scanning bins and selecting the bin with maximum volume. The script stores:
pocIndex: which bin has max volume
poc price: midpoint price of that bin
Value Area (VAH/VAL) using 70% volume
The script builds the value area around the POC outward until it captures 70% of total volume:
Start with the POC bin.
Expand one bin at a time to the side with more volume.
Stop when accumulated volume >= 70% of total profile volume.
Then:
VAL = rangeLow + binSize * lowerIdx
VAH = rangeLow + binSize * (upperIdx + 1)
This produces a classic “where most business happened” zone.
VWMP (Volume-Weighted Mean Price)
This is essentially the center of mass of the profile:
VWMP = sum(price * volume ) / totalVolume
It is similar in spirit to VWAP, but it is computed over the profile bins, not from bar-by-bar typical price.
Skewness calculation: turning the profile into an oscillator
This is the main feature.
1) Treat volumes as weights
For each bin:
weight = volume / totalVolume
Now weights sum to 1.
2) Compute weighted mean
Mean price:
mean = sum(weight * price )
3) Compute weighted variance and std deviation
Variance:
variance = sum(weight * (price - mean)^2)
stdDev = sqrt(variance)
4) Compute weighted third central moment
Third moment:
m3 = sum(weight * (price - mean)^3)
5) Standardize to skewness
Skewness:
rawSkew = m3 / (stdDev^3)
This standardization matters. Without it, the value would explode or shrink based on profile scale. Standardized skewness is dimensionless and comparable.
Smoothing and regime rules
Raw skewness can be jumpy because:
profile bins change as rangeHigh/rangeLow shift,
one high-volume candle can reshape the distribution,
volume regimes change quickly in crypto.
So the indicator applies EMA smoothing:
smoothedSkew = EMA(rawSkew, smooth)
Then it classifies regime using fixed thresholds:
Bullish (ACCUMULATION): smoothedSkew > +0.25
Bearish (DISTRIBUTION): smoothedSkew < -0.25
Neutral: between those values
Signals are generated on threshold cross events:
Bull signal when smoothedSkew crosses above +0.25
Bear signal when smoothedSkew crosses below -0.25
This makes the skew act like a regime oscillator rather than a constantly flipping color.
Volume Profile plotting modes
The script draws the profile on the last bar, using boxes for each bin, anchored to the right with a configurable offset. The width of each profile bar is normalized by max bin volume:
volRatio = binVol / maxVol
barWidth = volRatio * width
Three style modes exist:
1) Gradient
Uses a “jet-like” gradient based on volRatio (blue → red). Higher-volume bins stand out naturally. Transparency increases as volume decreases, so low-volume bins fade.
2) Solid
Uses the current regime color (bull/bear/neutral) for all bins, with transparency. This makes the profile read as “structure + regime.”
3) Skew Highlight
Highlights bins that match the skew bias:
If skew bullish, emphasize lower portion of profile.
If skew bearish, emphasize higher portion of profile.
Else, keep most bins neutral.
This is a visual “where the skew is coming from” mode.
Historical POC tracking and Naked POCs
This script also treats POCs as meaningful levels over time, similar to how traders track old VA levels.
What is a “naked POC”?
A “naked POC” is a previously formed POC that has not been revisited (retested) by price since it was recorded. Many traders watch these as potential reaction zones because they represent prior “maximum traded interest” that the market has not re-engaged with.
How this script records POCs
It stores a new historical POC when:
At least updatebars have passed since the last stored POC, and
The POC has changed by at least pochangethres (%) from the last stored value.
New stored POCs are flagged as naked by default.
How naked becomes tested
On each update, the script checks whether price has entered a small zone around a naked POC:
zoneSize = POC * 0.002 (about 0.2%)
If bar range overlaps that zone, mark it as tested (not naked).
Display controls:
Highlight Naked POCs: draws and labels untested POCs.
Show Tested POCs: optionally draw tested ones in a muted color.
To avoid clutter, the script limits stored POCs to the most recent 20 and avoids drawing ones too close to the current POC.
On-chart key levels and what they mean
When enabled, the script draws the current lookback profile levels on the price chart:
POC (solid): the “most traded” price.
VAH/VAL (dashed): boundaries of the 70% value area.
VWMP (dotted): volume-weighted mean of the profile distribution.
Interpretation framework (practical, not mystical):
POC often behaves like a magnet in balanced conditions.
VAH/VAL define the “accepted” area, breaks can signal auction continuation.
VWMP is a fair-value reference, useful as a mean anchor when skew is neutralizing.
Oscillator panel and histogram
The skew oscillator is plotted in a separate pane:
Line: smoothedSkew, colored by regime.
Histogram: smoothedSkew as bars, colored by sign.
Fill: subtle shading above/below 0 to reinforce bias.
This makes it easy to read:
Direction of bias (positive vs negative).
Strength (distance from 0 and from thresholds).
Transitions (crosses of ±0.25).
Info table: what it summarizes
On the last bar, a table prints key diagnostics:
Current skew value (smoothed).
Regime label (ACCUMULATION / DISTRIBUTION / NEUTRAL).
Current POC, VAH, VAL, VWMP.
Count of naked POCs still active.
A simple “volume location” hint (lower/higher/balanced).
This is designed for quick scanning without reading the entire profile.
Alerts
The indicator includes alerts for:
Skew regime shifts (cross above +0.25, cross below -0.25).
Price crossing above/below current POC.
Approaching a naked POC (within 1% of any active naked POC).
The “approaching naked POC” alert is useful as a heads-up that price is entering a historically important volume magnet/reaction zone.
How to use it properly
1) Regime filter
Use skew regime to decide what type of trades you should prioritize:
ACCUMULATION (positive skew): market activity is heavier at lower prices, pullbacks into value or below VWMP often matter more.
DISTRIBUTION (negative skew): activity is heavier at higher prices, rallies into value or above VWMP often matter more.
NEUTRAL: mean-reversion and POC magnet behavior tends to dominate.
This is not “buy when green.” It is context for what the auction is doing.
2) Level-based execution
Combine skew with VA/POC levels:
In neutral regimes, expect rotations around POC and inside VA.
In strong skew regimes, watch for acceptance away from POC and reactions at VA edges.
3) Naked POCs as targets and reaction zones
Naked POCs can act like unfinished business. Common workflows:
As targets in rotations.
As areas to reduce risk when price is approaching.
As “if it breaks cleanly, trend continuation” markers when price returns with force.
Parameter tuning guidance
Lookback
Controls how “local” the profile is.
Shorter: reacts faster, more sensitive to recent moves.
Longer: more stable, better for swing context.
Bins
Controls resolution of the profile.
Higher bins: more detail, more computation, more sensitive profile shape.
Lower bins: smoother, less detail, more stable skew.
Smoothing
Controls how noisy the skew oscillator is.
Higher smoothing: fewer regime flips, slower response.
Lower smoothing: more responsive, more false transitions.
POC tracking settings
Update interval and threshold decide how many historical POCs you store and how different they must be. If you set them too loose, you will spam levels. If too strict, you will miss meaningful shifts.
Limitations and what not to assume
This indicator uses candle-range volume distribution because Pine cannot see tick-level volume-at-price. That means:
The profile is an approximation of where volume could have traded, not exact tape data.
Skew is best treated as a structural bias, not a precise signal generator.
Extreme single-bar events can distort the distribution briefly, smoothing helps but cannot remove reality.
Summary
Volume Profile Skew takes standard volume profile structure (POC, Value Area, volume-weighted mean) and adds a statistically grounded measure of profile shape using skewness. The result is a regime oscillator that quantifies whether volume concentration is leaning toward lower prices (accumulation) or higher prices (distribution), while also plotting the full profile, key levels, and historical naked POCs for actionable context.
Indicator
Indicator
[LJ] HTF Candles with Volume POC [Highly Optimized]Welcome to the ultimate Higher Timeframe (HTF) fusion tool.
This indicator seamlessly merges Higher Timeframe price action with precise volume profiling on Lower Timeframe (LTF) charts. By utilizing advanced array memory management, it bypasses PulseWire's drawing limits to render clean HTF candle boxes and their exact Volume Point of Control (POC), ensuring peak performance and accuracy even on extreme timeframe combinations.
🔎 Visual Guide: What's on Your Chart?
The Big Boxes (Wick & Body): These represent the Open, High, Low, and Close of your selected Higher Timeframe (e.g., a 1-Hour candle projected onto your 5-Minute chart). Color-coded for bullish/bearish momentum.
The Midline: A horizontal line dividing the HTF candle perfectly in half. Useful for gauging if LTF price is in a "premium" or "discount" zone relative to the HTF.
The Yellow Box (The POC): This is the Point of Control. When an HTF candle closes, this yellow box locks in place, showing the exact price zone that saw the highest volume accumulation during that period, calculated using precise LTF hlc3 price data.
⚙️ Key Settings & Configuration
To get the most out of this indicator, check these inputs in the settings menu:
Box Time Interval: Set this to your desired HTF (e.g., "1H", "4H", "D").
Tip: A good rule of thumb is an HTF that is 5x to 12x higher than your current chart.
POC Resolution (Levels): This is your fine-tuning dial.
Set to 50 - 100: Creates a very thin, precise POC line. Great for exact support/resistance levels.
Set to 15 - 35: Creates a thicker POC "Zone". Great for capturing broader areas of high liquidity.
Max Historical Boxes: Controls how far back the indicator draws. Keep this at 50 for a clean chart and fast loading times.
📈 Trading Strategies: How to Trade the HTF POC
1. The "Magnet" Mean Reversion High volume nodes (POCs) act as price magnets. If the current LTF price is far away from the previous HTF candle's POC, look for setups that trade back toward that yellow box.
2. The Break & Retest The POC box acts as a heavy Support/Resistance wall. If price breaks through the yellow POC box with strong momentum, look to enter on the retest of that box in the direction of the breakout.
3. Trend Continuation In a strong uptrend, look for the current LTF price to bounce off the Midline or the previous candle's POC to join the dominant HTF trend.
⚡ Performance Note
This indicator uses advanced Pine Script Array Management. Unlike standard volume profile scripts that crash when calculating big timeframes, this indicator guarantees zero lag and no disappearing boxes, even when looking at a Daily HTF on a 1-Minute chart.
🙏 Credits
This is a refactored and heavily optimized fusion of two great concepts:
Original HTF Box logic by © krollo041
Original Volume Thermometer concept by © ChartPrime
Merged, debugged, and optimized for v6 Array Management by ©Luki_eR
Disclaimer: This script is for educational and informational purposes only and does not constitute financial advice. Indicator
Indicator
Indicator
[Greeny] RTH Only Naked VPOCWhat it does
Calculates and displays daily Volume Point of Control (VPOC) levels based on RTH (Regular Trading Hours) session only. Tracks which VPOCs remain "naked" (untouched) and which have been hit - but only counts hits during RTH hours, ignoring overnight/globex touches.
Key Features
One VPOC per trading day calculated from entire RTH session volume profile
RTH-only hit detection - levels only marked as hit when touched during RTH, not overnight
Works on all timeframes - daily, hourly, or any chart timeframe
Volume-based filtering - automatically skips low-liquidity sessions (pre-front-month contract data)
Visual markers - small dash on origin bar shows where each VPOC was, even after being hit
Visual Guide
Yellow dashed line - Naked VPOC (not yet touched during RTH)
White dashed line - Hit VPOC (was touched during RTH)
Small dash on candle - POC origin marker
Settings
Display options: Toggle to show only naked POCs, customize hit/naked colors, adjust line width and style (solid/dashed/dotted), enable/disable line extension and origin markers.
RTH Session: Configure start and end time in NY timezone. Default is 9:30-16:00 (US equity market hours), which equals 15:30-22:00 Budapest time.
Advanced: Adjust volume profile resolution (default 250 bins), data source timeframe for calculations (5min recommended for daily charts), and minimum volume threshold to filter out low-liquidity sessions like pre-rollover contract data (default 10% of average).
Best For
ES/MES, NQ/MNQ futures traders
Mean reversion strategies using VPOC as support/resistance
Auction Market Theory practitioners
Anyone wanting clean RTH-only volume profile levels
Note on Contract Rollovers
When using specific contract symbols (e.g., ESH2026 instead of ES1!), the script may show many naked VPOCs from months before the contract became active. This happens because futures contracts have very low liquidity before becoming the front-month, creating unreliable VPOCs with gaps that never get hit. The volume filter helps reduce this, but you may need to increase the "Min Volume % of Average" setting or simply ignore older levels when viewing back-month data. Indicator
Volume Cluster Profile [VCP] (Zeiierman)█ Overview
Volume Cluster Profile (Zeiierman) is a volume profile tool that builds cluster-enhanced volume-by-price maps for both the current market window and prior swing segments.
Instead of treating the profile as a raw histogram only, VCP detects the dominant volume peaks (clusters) inside the profile, then uses a Gaussian spread model to “radiate” those peaks into surrounding price bins. This produces a smoother, more context-aware profile that highlights where volume is most meaningfully concentrated, not just where it happened to print.
On top of the live profile, VCP automatically records historical swing profiles between pivots, wraps each segment for clarity, and can project the most recent segment’s High/Low Value extensions (VA/LV) forward to the current bar to keep key structure visible as price evolves.
█ How It Works
⚪ 1) Profile Construction (Volume-by-Price)
VCP builds a volume profile histogram over a chosen window (current lookback, or a swing segment):
Range Scan
The script finds the full min → max price range inside the window.
Bin the Range
That range is divided into a user-defined number of Price Bins (rows). More bins = finer detail, but heavier computation.
Accumulate Volume into Bins
For each bar inside the window, the script takes the bar’s close price, determines which price bin it belongs to, and adds the bar’s volume to that bin.
float step = (maxPrice - minPrice) / binsCount
for i = 0 to barsToUse - 1
int b = f_clamp(int(math.floor((close - minPrice) / step)), 0, binsCount - 1)
volBins += volume
Result: volBins becomes a standard volume-by-price histogram (close-based binning).
⚪ 2) Cluster Detection (Finding Dominant Peaks)
Once the raw histogram is built, VCP identifies cluster centers as the most meaningful volume “hills”:
Local Peak Test
A bin becomes a cluster candidate if its volume is greater than or equal to its immediate neighbors (left/right).
Filter Weak Peaks
Peaks must also be above a basic activity threshold (relative to the average bin volume) to avoid noise.
bool isPeak = v >= left and v >= right
if isPeak and v > avgVol
array.push(clusterIdxs, b)
Keep the Best Peaks Only
If too many peaks exist, the script keeps only the strongest ones, capped by: Max Cluster Centers
Result: clusterIdxs = the set of dominant profile peaks (cluster centers).
⚪ 3) Cluster Enhancement (Gaussian Spread Model)
This is what makes VCP different from a raw profile.
Instead of using volBins directly, the script builds an enhanced profile where each cluster center influences nearby price bins using a Gaussian curve:
Distance from each bin to each cluster center is computed in “bin units”
A Gaussian weight is applied so that bins near the center receive stronger influence, while bins farther away decay smoothly.
Cluster Spread (sigma) controls how wide this influence reaches: low sigma produces tight, sharp clusters, while high sigma results in wider, smoother structure zones.
enhanced += centerV * math.exp(-(dist*dist) / (2.0 * clusterSigma * clusterSigma))
volBinsAI := enhanced / szClFinal
Result: volBinsAI = the cluster-enhanced volume value for each bin.
In practice, VCP turns the profile into a structure map of dominant volume concentrations, rather than a simple “where volume printed” histogram.
⚪ 4) POC from the Enhanced Profile
After enhancement:
The bin with the highest volBinsAI becomes the POC (Point of Control)
POC is plotted at the midpoint price of that bin
if volBinsAI > maxVol
maxVol := volBinsAI , pocBin := b
So the POC reflects the cluster-enhanced profile rather than the raw histogram.
█ How to Use
⚪ Read Cluster Structure (Default = 2 Clusters)
By default, the Volume Cluster Profile (VCP) is configured to detect up to 2 dominant volume clusters within the profile. These clusters represent price zones where the market accepted trading activity, not just where volume printed randomly.
⚪ When TWO Clusters Appear
When VCP detects two distinct clusters, it usually indicates:
Two competing areas of value
Ongoing auction between higher and lower acceptance zones
Treat each cluster as an acceptance zone
Expect slower price action and rotation inside clusters
Expect faster movement in the low-volume space between clusters
Use cluster-to-cluster movement as:
rotation targets
range boundaries
acceptance vs rejection tests
Typical behavior:
Price enters a cluster → stalls, consolidates, rotates
Price rejects at cluster edge → moves toward the opposite cluster
⚪ When ONLY ONE Cluster Appears
If VCP detects only one cluster, or if two clusters visually merge into one:
Volume is no longer split
The market has formed a single dominant value area
Price consensus is strong
Treat the cluster as the primary value anchor
Expect pullbacks and reactions around this zone
Bias becomes directional:
Above the cluster → bullish context
Below the cluster → bearish context
Inside the cluster → balance/chop
This structure often appears during clean trends or stable equilibria.
⚪ VA/LV Extensions
VCP projects two zones from the end of the most recent swing segment:
VA extension = the segment’s highest enhanced-volume bin (dominant zone)
LV extension = the segment’s lowest enhanced-volume bin (thin/weak zone)
A breakout of the VA extension signals acceptance and potential continuation. A retest of the VA or LV extension is used to confirm acceptance or rejection, while rejection from either zone often leads to rotation back toward value.
█ Settings
Cluster Volume Profile
Lookback Bars – how many recent bars build the current profile
Price Bins – profile resolution (more bins = more detail, heavier CPU)
Cluster Spread – Gaussian sigma; higher values widen/smooth cluster influence
Max Cluster Centers – cap on detected peaks used in enhancement
Historical Swing Cluster Volume Profile
Pivot Length – swing sensitivity (larger = fewer, broader segments)
Max Profiles – how many historical segments to retain
Profile Width – thickness of each historical profile
High & Low Value Area
Profile VA/LV – extend the last segment’s top-bin and low-bin zones forward
-----------------
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.
Indicator
Indicator
Point of Control [BigBeluga]🔵 OVERVIEW
Point of Control identifies the exact price level with the highest traded volume over a selected lookback period.
This level—called the Point of Control (PoC) —marks where the greatest market participation occurred, representing a zone of highest volume.
The indicator helps traders visualize dominant volume concentrations, fair-value levels, and structural balance within recent price action.
🔵 CONCEPTS
Point of Control (PoC) — The single price level within the defined lookback range that has accumulated the most traded volume.
Volume Distribution Bins — The price range is divided into 25 equal bins, and volume is aggregated per bin to locate the maximum concentration.
Range Boundaries — The highest and lowest price within the lookback window are used to form the upper and lower reference limits.
PoC Channel — Optional upper and lower bands plotted around the main PoC to visualize a fair-value corridor.
Volume Intensity Mapping — Candle color dynamically shifts based on the candle’s position relative to the PoC channel, showing whether price is balanced or trending away from high-volume levels.
🔵 FEATURES
Configurable Lookback Range — Adjust how many bars (10–400) are used for calculating the PoC.
Precise PoC Calculation — Volume aggregation across 25 bins to identify the exact volume peak.
Dynamic Channel Visualization — PoC bands above and below the central level to indicate equilibrium tolerance.
Adaptive Candle Coloring —
- Neutral → price inside PoC channel. Gray
- Bullish → price above PoC channel. Blue 🔵
- Bearish → price below PoC channel. Orange 🟠
Automatic Volume Labeling — Displays total volume at the active PoC level for quick reference.
Directional Indicators — 🔵 or 🟠 markers appear when price shifts above or below the PoC channel.
Range Visualization — Plots the highest and lowest points of the active lookback window for contextual awareness.
Live Updating Logic — PoC recalculates automatically every 15 bars for efficient chart performance and accuracy.
🔵 HOW TO USE
Volume Anchoring — Use PoC as a reference for where the majority of volume occurred; price often reacts to or consolidates around this level.
Trend Confirmation — Sustained price movement away from PoC channel may signal developing directional imbalance.
Value Tracking — Watch the shifting of PoC across time to identify where fair value migrates during market evolution.
Equilibrium Mapping — When price hovers around PoC, the market is balanced; when it departs, a new value zone may form.
Combine With Volume Profiles — Use alongside profile tools for higher-resolution analysis of institutional activity.
🔵 CONCLUSION
Point of Control provides a pure, volume-centric view of market balance by pinpointing where most transactions occurred within any chosen range.
It delivers a clean and efficient visualization of fair value zones—helping traders track the heartbeat of market participation, recognize dominant liquidity areas, and stay aligned with where true market interest resides. Indicator
Indicator
HTF Frequency Zone [BigBeluga]🔵 OVERVIEW
HTF Frequency Zone highlights the dominant price level (Point of Control) and the full high–low expansion of any higher timeframe — Daily, Weekly, or Monthly. It captures the frequency of closes inside each HTF candle and plots the most traded “frequency zone”, allowing traders to easily see where price spent the most time and where buy/sell pressure accumulated.
This tool transforms each higher-timeframe bar into a fully visualized structure:
• Top = HTF high
• Bottom = HTF low
• Midline = HTF Frequency POC
• Color-coded zones = bullish or bearish bias
• Labels = counts of bullish and bearish candles inside the HTF range
It is designed to give traders an immediate understanding of high-timeframe balance, imbalance, and price attraction zones.
🔵 CONCEPTS
HTF Partitioning — Each Weekly/Daily/Monthly candle is converted into a dedicated zone with its own High, Low, and Frequency Point of Control.
Frequency POC (Most Touched Price) — The indicator divides the HTF range into 100 bins and counts how many times price closed near each level.
Dominant Zone — The level with the highest frequency becomes the HTF “Value Zone,” plotted as a bold central line.
Directional Bias —
• Bullish HTF zone
• Bearish HTF zone
Internal Candle Counting — Within each HTF period the indicator counts:
• Buy candles (close > open)
• Sell candles (close < open)
This reveals whether intraperiod flow was bullish or bearish.
HTF Structure Blocks — High, Low, and POC are connected across the entire higher-timeframe duration, showing the real shape of HTF balance.
🔵 FEATURES
Automatic HTF Zone Construction — Generates a complete price zone every time the selected timeframe flips (Daily / Weekly / Monthly).
Dynamic High & Low Extraction — The indicator scans every bar inside the HTF window to find true extremes of the range.
100-Level Frequency Scan — Each close within the period is assigned to a bin, creating a detailed distribution of price interaction.
HTF POC Highlighting — The most frequent price level is plotted with a bold red line for immediate visual clarity.
Bull/Bear Coloring —
• Green → Bullish HTF zone.
• Orange → Bearish HTF zone.
Zone Shading — High–Low range is filled with a semi-transparent color matching trend direction.
Buy/Sell Candle Counters — Printed at the top and bottom of each HTF block, showing how many internal candles were bullish or bearish.
POC Label — Displays frequency count (how many touches) at the POC level.
Adaptive Threshold Warning — If bars inside the HTF window are too few (<10), the indicator warns the trader to switch timeframe.
🔵 HOW TO USE
Higher-Timeframe Biasing — Read the zone color to determine if the HTF candle leaned bullish or bearish.
Value Zone Reactions — Price often reacts to the Frequency POC; use it as support/resistance or liquidity magnet.
Range Context — Identify when price is trading near HTF highs (breakout potential) or lows (reversal potential).
Momentum Evaluation — More bullish internal candles = internal buying pressure; more bearish = internal selling pressure.
Swing Trading — Use HTF zones as the “macro map,” then execute trades on lower timeframes aligned with the zone structure.
Liquidity Awareness — The HTF POC often aligns with algorithmic liquidity levels, making it a strong reaction point.
🔵 CONCLUSION
HTF Frequency Zone transforms raw higher-timeframe candles into detailed distribution zones that reveal true market behavior inside the HTF structure. By showing highs, lows, buying/selling activity, and the most interacted price level (Frequency POC), this tool becomes invaluable for traders who want to align executions with powerful HTF levels, liquidity magnets, and structural zones. Indicator
PoC Migration Map [BackQuant]PoC Migration Map
A volume structure tool that builds a side volume profile, extracts rolling Points of Control (PoCs), and maps how those PoCs migrate through time so you can see where value is moving, how volume clusters shift, and how that aligns with trend regime.
What this is
This indicator combines a classic volume profile with a segmented PoC trail. It looks back over a configurable window, splits that window into bins by price, and shows you where volume has concentrated. On top of that, it slices the lookback into fixed bar segments, finds the local PoC in each segment, and plots those PoCs as a chain of nodes across the chart.
The result is a "migration map" of value:
A side volume profile that shows how volume is distributed over the recent price range.
A sequence of PoC nodes that show where local value has been accepted over time.
Lines that connect those PoCs to reveal the path of value migration.
Optional trend coloring based on EMA 12 and EMA 21, so each PoC also encodes trend regime.
Used together, this gives you a structural read on where the market has actually traded size, how "value" is moving, and whether that movement is aligned or fighting the current trend.
Core components
Lookback volume profile - a side histogram built from all closes and volumes in the chosen lookback window.
Segmented PoC trail - rolling PoCs computed over fixed bar segments, plotted as nodes in time.
Trend heatmap - optional color mapping of PoC nodes using EMA 12 versus EMA 21.
PoC labels - optional labels on every Nth PoC for easier reading and referencing.
How it works
1) Global lookback and binning
You choose:
Lookback Bars - how far back to collect data.
Number of Bins - how finely to split the price range.
The script:
Finds the highest high and lowest low in the lookback.
Computes the total price range and divides it into equal binCount slices.
Assigns each bar's close and volume into the appropriate price bin.
This creates a discretized volume distribution across the entire lookback.
2) Side volume profile
If "Show Side Profile" is enabled, a right-hand volume profile is drawn:
Each bin becomes a horizontal bar anchored at a configurable "Right Offset" from the current bar.
The horizontal width of each bar is proportional to that bin's volume relative to the maximum volume bin.
Optionally, volume values and percentages are printed inside the profile bars.
Color and transparency are controlled by:
Base Profile Color and its transparency.
A gradient that uses relative volume to modulate opacity between lower volume and higher volume bins.
Profile Width (%) - how wide the maximum bin can extend in bars.
This gives you an at-a-glance view of the volume landscape for the chosen lookback window.
3) Segmenting for PoC migration
To build the PoC trail, the lookback is divided into segments:
Bars per Segment - bars in each local cluster.
Number of Segments - how many segments you want to see back in time.
For each segment:
The script uses the same price bins and accumulates volume only from bars in that segment.
It finds the bin with the highest volume in that segment, which is the local PoC for that segment.
It sets the PoC price to the center of that bin.
It finds the "mid bar" of the segment and places the PoC node at that time on the chart.
This is repeated for each segment from older to newer, so you get a chain of PoCs that shows how local value has migrated over time.
4) Trend regime and color coding
The indicator precomputes:
EMA 12 (Fast).
EMA 21 (Slow).
For each PoC:
It samples EMA 12 and EMA 21 at the mid bar of that segment.
It computes a simple trend score as fast EMA minus slow EMA.
If trend heatmap is enabled, PoC nodes (and the lines between them) are colored by:
Trend Up Color if EMA 12 is above EMA 21.
Trend Down Color if EMA 12 is below EMA 21.
Trend Flat Color if they are roughly equal.
If the trend heatmap is disabled, PoC color is instead based on PoC migration:
If the current PoC is above the previous PoC, use the Up PoC Color.
If the current PoC is below the previous PoC, use the Down PoC Color.
If unchanged, use the Flat PoC Color.
5) Connecting PoCs and labels
Once PoC prices and times are known:
Each PoC is connected to the previous one with a dotted line, using the PoC's color.
Optional labels are placed next to every Nth PoC:
Label text uses a simple "PoC N" scheme.
Label background uses a configurable label background color.
Label border is colored by the PoC's own color for visual consistency.
This turns the PoCs into a visual path that can be read like a "value trajectory" across the chart.
What it plots
When fully enabled, you will see:
A right-sided volume profile for the chosen lookback window, built from volume by price.
Colored horizontal bars representing each price bin's relative volume.
Optional volume text showing each bin's volume and its percentage of the profile maximum.
A series of PoC nodes spaced across the chart at the mid point of each segment.
Dotted lines connecting those PoCs to show the migration path of value.
Optional PoC labels at each Nth node for easier reference.
Color-coding of PoCs and lines either by EMA 12 / 21 trend regime or by up/down PoC drift.
Reading PoC migration and market pressure
Side profile as a pressure map
The side profile shows where trading has been most active:
Thick, opaque bars represent high volume zones and possible high interest or acceptance areas.
Thin, faint bars represent low volume zones, potential rejection or transition areas.
When price trades near a high volume bin, the market is sitting on an area of prior acceptance and size.
When price moves quickly through low volume bins, it often does so with less friction.
This gives you a static map of where the market has been willing to do business within your lookback.
PoC trail as a value migration map
The PoC chain represents "where value has lived" over time:
An upward sloping PoC trail indicates value migrating higher. Buyers have been willing to transact at increasingly higher prices.
A downward sloping trail indicates value migrating lower and sellers pushing the center of mass down.
A flat or oscillating trail indicates balance or rotational behaviour, with no clear directional acceptance.
Taken together, you can interpret:
Side profile as "where the volume mass sits", a static pressure field.
PoC trail as "how that mass has moved", the dynamic path of value.
Trend heatmap as a regime overlay
When PoCs are colored by the EMA 12 / 21 spread:
Green PoCs mark segments where the faster EMA is above the slower EMA, that is, a local uptrend regime.
Red PoCs mark segments where the faster EMA is below the slower EMA, that is, a local downtrend regime.
Gray PoCs mark flat or ambiguous trend segments.
This lets you answer questions like:
"Is value migrating higher while the trend regime is also up?" (trend confirming value).
"Is value migrating higher but most PoCs are red?" (value against the prevailing trend).
"Has value started to roll over just as PoCs flip from green to red?" (early regime transition).
Key settings
General Settings
Lookback Bars - how many bars back to use for both the global volume profile and segment profiles.
Number of Bins - how many price bins to split the high to low range into.
Profile Settings
Show Side Profile - toggle the right-hand volume profile on or off.
Profile Width (%) - how wide the largest volume bar is allowed to be in terms of bars.
Base Profile Color - the starting color for profile bars, with transparency.
Show Volume Values - if enabled, print volume and percent for each non-zero bin.
Profile Text Color - color for volume text inside the profile.
PoC Migration Settings
Show PoC Migration - toggle the PoC trail plotting.
Bars per Segment - the number of bars contained in each segment.
Number of Segments - how many segments to build backwards from the current bar.
Horizontal Spacing (bars) - spacing between PoC nodes when drawn. (Used to separate PoCs horizontally.)
Label Every Nth PoC - draw labels at every Nth PoC (0 or 1 to suppress labels).
Right Offset (bars) - horizontal offset to anchor the side profile on the right.
Up PoC Color - color used when a PoC is higher than the previous one, if trend heatmap is off.
Down PoC Color - color used when a PoC is lower than the previous one, if trend heatmap is off.
Flat PoC Color - color used when the PoC is unchanged, if trend heatmap is off.
PoC Label Background - background color for PoC labels.
Trend Heatmap Settings
Color PoCs By Trend (EMA 12 / 21) - when enabled, overrides simple up/down coloring and uses EMA-based trend colors.
Fast EMA - length for the fast EMA.
Slow EMA - length for the slow EMA.
Trend Up Color - color for PoCs in a bullish EMA regime.
Trend Down Color - color for PoCs in a bearish EMA regime.
Trend Flat Color - color for neutral or flat EMA regimes.
Trading applications
1) Value migration and trend confirmation
Use the PoC path to see if value is following price or lagging it:
In a healthy uptrend, price, PoCs, and trend regime should all lean higher.
In a weakening trend, price may still move up, but PoCs flatten or start drifting lower, suggesting fewer participants are accepting the new highs.
In a downtrend, persistent downward PoC migration confirms that sellers are winning the value battle.
2) Identifying acceptance and rejection zones
Combine the side profile with PoC locations:
High volume bins near clustered PoCs mark strong acceptance zones, good areas to watch for re-tests and decision points.
PoCs that quickly jump across low volume areas can indicate rejection and fast repricing between value zones.
High volume zones with mixed PoC colors may signal balance or prolonged negotiation.
3) Structuring entries and exits
Use the map to refine trade location:
Fade trades against value migration are higher risk unless you see clear signs of exhaustion or regime change.
Pullbacks into prior PoC zones in the direction of the current PoC slope can offer higher quality entries.
Stops placed beyond major accepted zones (clusters of PoCs and high volume bins) are less likely to be hit by random noise.
4) Regime transitions
Watch how PoCs behave as the EMA regime changes:
A flip in EMA 12 versus EMA 21, coupled with a turn in PoC slope, is a strong signal that value is beginning to move with the new trend.
If EMAs flip but PoC migration does not follow, the trend signal may be early or false.
A weakening PoC path (lower highs in PoCs) while trend colors are still green can warn of a late-stage trend.
Best practices
Start with a moderate lookback such as 200 to 300 bars and a moderate bin count such as 20 to 40. Too many bins can make the profile overly granular and sparse.
Align "Bars per Segment" with your trading horizon. For example, 5 to 10 bars for intraday, 10 to 20 bars for swing.
Use the profile and PoC trail as structural context rather than as a direct buy or sell signal. Combine with your existing setups for timing.
Pay attention to clusters of PoCs at similar prices. Those are areas where the market has repeatedly accepted value, and they often matter on future tests.
Notes
This is a structural volume tool, not a complete trading system. It does not manage execution, position sizing or risk management. Use it to understand:
Where the bulk of trading has occurred in your chosen window.
How the center of volume has migrated over time.
Whether that migration is aligned with or fighting the current trend regime.
By turning PoC evolution into a visible path and adding a trend-aware heatmap, the PoC Migration Map makes it easier to see how value has been moving, where the market is likely to feel "heavy" or "light", and how that structure fits into your trading decisions.
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Balanced Delta Volume Profile (Zeiierman)█ Overview
Balanced Delta Volume Profile (Zeiierman) builds a vertical, price-by-price profile that blends total participation with balance quality. Instead of plotting raw volume alone, it weights each price bin by:
how balanced buyers vs. sellers were,
how compressed price was inside that bin,
how often price revisited it.
The result spotlights fair value and acceptance zones while still revealing momentum/imbalance areas—ideal for reading rotation vs. trend, continuation vs. exhaustion, and the prices that truly matter.
Highlights
Balanced score that fuses delta symmetry, price compression, and hit frequency.
Optional heat spectrum for instant read of participation density and balance strength.
POC-like auto highlight of the dominant price level within the lookback window.
Works across timeframes for session profiling, swing context, or regime shifts.
█ How It Works
⚪ Profile Construction
The script scans a fixed History Length and divides the full high–low span into Bin Count price bins. For every bar in the window, its volume is proportionally distributed across the bins it overlaps, so wide-range bars contribute across multiple bins, while narrow bars concentrate where they traded most. This yields per-bin totals for:
Total Volume (participation)
Positive / Negative Volume (up vs. down bar contribution)
Hit Count (how often price touched the bin)
Average Price Range (mean bar range inside the bin; a proxy for compression)
⚪ Delta & Direction
For each bin, delta symmetry is measured via the ratio of |pos − neg| to total volume. Bins with balanced two-sided flow score higher than one-sided, runaway bins. This curbs the tendency of raw volume profiles to over-reward impulsive bursts.
⚪ Balance Score
Each price bin gets a balance score that multiplies three normalized components:
Delta Balance: rewards bins where buy/sell pressure is symmetrical (configurable via Volume Momentum Weight).
Price Compression: rewards bins where average bar range is relatively small (configurable via Price Momentum Weight).
Durability: rewards bins revisited often (configurable via Hits Weight).
A Min Hits Filter removes flimsy, single-touch bins from dominating the score. The profile can display pure totals or Average Mode (Vol/Hit) to compare bins fairly when hit counts differ.
⚪ Display & Heat Spectrum
The final plotted bar length per bin is the display volume (total or average) weighted by the balance score and normalized to 100.
POC-like Highlight: The 100% bin is outlined (and labeled) when Highlight Max Volume Bin is ON.
Heat Spectrum (optional): A background gradient scales with normalized bar length and balance hue.
Balance Hue: Interpolates between Balance Low/High Colors so high-balance bins visually pop as “accepted value.”
█ How to Use
The profile is effectively a map of price acceptance:
High, bright bars = strong participation at balanced prices → fair value/rotation zones.
Thin, muted bars = poor acceptance → imbalance or transition areas.
POC-style level = most influential price in the lookback window.
⚪ Find Fair Value & Acceptance
Thick, high-balance bins mark value. Expect rotation: price often revisits or oscillates around these areas. They’re prime zones for mean-reversion fades, scale-ins, and risk-defined trades against the edges.
⚪ Identify Imbalance & Funnels
Low-balance, low-hit bins often act like air pockets—price can move through them quickly. These zones are helpful for continuation trades into thin areas or for timing breakout pulls back into acceptance.
⚪ POC Dynamics
When price leaves the POC and returns, watch for re-acceptance (price comes back into the POC or high-balance zone and stays there.) vs. rejection (trend continuation away from value). The auto-highlight makes this quick to judge.
█ Settings
History Length – Bars scanned for the profile. Longer = broader context, slower to adapt.
Bin Count – Vertical resolution of bins between the window’s min and max price.
Display Shift – Offsets the rendering rightward for clarity.
Average Mode (Vol/Hit) – ON uses average volume per visit; OFF uses total volume.
Volume Momentum Weight – Emphasizes two-way flow; higher values favor balanced bins over one-sided deltas.
Price Momentum Weight – Emphasizes compression; higher values favor narrow-range, coiling price action.
Hits Weight – Rewards bins revisited often; higher values favor durable acceptance.
Min Hits Filter – Minimum visits a bin needs to qualify for the balance score.
Show Heat Spectrum – Background gradient for quick read of density and balance.
Highlight Max Volume Bin – Outline + raw volume label for the dominant bin.
Max Volume Color – Color used for that highlight.
Balance Low/High Colors – Gradient endpoints for balance hue across the profile.
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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.
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