Volume Footprint: Measuring by Math & GeometryIntroduction
Volume tells you how much traded. A footprint tells you where.
A conventional volume bar compresses all trading activity within a candle into a single total. That total measures participation, but it does not show how the activity was distributed through price. Two candles can carry similar volume and range while containing very different internal structures.
A volume footprint restores this missing dimension. It organizes a bar's volume across price levels and separates buying from selling, so the candle becomes a readable column: where activity concentrated, where one side dominated, and where the two sides met.
This price-level structure is the basis for the measurements and diagnostics used throughout this indicator.
1. The volume footprint
As introduced above, a footprint reveals the role and position of the real participants inside a candle. The diagram below illustrates the structure:
The diagram shows the footprint structure of a single candle: volume is distributed across price levels and separated into sell and buy activity. This makes the internal distribution of participation visible instead of reducing the candle to one total volume figure.
The real challenge in constructing a footprint is how buy and sell volume are derived. TradingView’s native Footprint data provides the highest-fidelity source used by this indicator, but it is available only on Premium and Ultimate plans. To make the concept accessible to users on other plans, we therefore estimate buy and sell volume using the alternative methods described below. These methods are approximations, intended to explain and visualize footprint mechanics—not to claim the same precision as TradingView’s native Footprint data.
Buy/Sell Volume Calculation Engines
The indicator uses three methods as engines for calculating buy and sell volume. These engines are selected from the Engine option in the indicator settings and will be explained individually below.
- Geometric
- Intrabar
- Footprint
A. Geometric Calculation Engine
The Geometric engine estimates buy and sell volume from the position of the candle’s close within its high–low range. A close nearer the high assigns a larger share to buying volume, while a close nearer the low assigns a larger share to selling volume.
As shown in the image, the Geometric engine is an estimation model based on the position of the candle’s close within its high–low range. Buy and sell volume are calculated as:
If the candle has no range, volume is divided equally between buying and selling.
It is important to emphasize that this is an estimated representation of buy and sell activity, not true transaction-side volume. However, it can still provide a useful analytical framework for markets where reported volume is incomplete, fragmented, or not representative of a centralized market-wide feed, such as many cryptocurrency markets and spot gold.
B. Intrabar Calculation Engine
The Intrabar engine derives buy and sell volume from lower-timeframe activity inside each chart bar, using TradingView’s official ta library. Instead of estimating the split from the shape of the parent candle, it aggregates the lower-timeframe up and down volume returned by the library.
Higher intrabar resolution generally provides greater precision, but also reduces the amount of historical coverage available. Tick-level data offers the finest resolution where available, while a 1-minute lower timeframe provides a practical alternative that can also be used on lower TradingView plans. On higher chart timeframes, such as 4-hour or daily charts, the 1-minute series can provide a substantially finer approximation of the internal buy/sell structure than calculations based only on the parent candle.
In practical terms, Intrabar provides the middle ground between simple candle-based estimation and native Footprint data: more granular than Geometric, yet still accessible across a wider range of TradingView plans. Its effectiveness ultimately depends on how much lower-timeframe detail is available for the chart being analyzed.
C. Footprint Calculation Engine
The Footprint engine reads TradingView’s native volume-footprint data directly. Unlike the Geometric and Intrabar engines, buy and sell volume are not inferred from candle geometry or lower-timeframe direction; they are obtained from TradingView’s own price-level footprint structure.
This provides the highest-fidelity volume source used by the indicator and serves as the reference engine for footprint-based measurements. Access to this data requires a TradingView Premium or Ultimate plan.
How are the footprint price rows constructed when native row-level data is available only through TradingView’s Footprint engine on Premium and Ultimate plans?
For the native Footprint engine, each price row is read directly from TradingView together with its buy and sell volume. For the Geometric and Intrabar engines, only bar-level buy and sell totals are available, so these totals must be distributed across the candle’s price range. To accomplish this, the indicator uses a Gaussian distribution model.
What Is the Gaussian Distribution Model?
A Gaussian distribution is a bell-shaped mathematical model in which the greatest weight is concentrated around a central value and progressively decreases as distance from that center increases. In this indicator, it is used as a weighting model to distribute a bar’s total buy and sell volume across its price rows, rather than assigning the same amount of volume to every level.
The simplified diagrams below illustrate the basic idea before we apply it separately to the buy and sell sides of a candle.
Figure 1 — A single bell distributes one total; no volume is created or lost
Figure 2 — Both bells cover the full range; only their centers differ, and each sums back to its own total
As the Volume Engine and its role in the settings menu were explained earlier, we will follow the same approach here for the Footprint structure and Gaussian model. The relevant settings will be introduced alongside these concepts so their practical effect on the indicator can be understood directly.
Ticks Per Row
This setting controls the row size only for TradingView’s native Footprint engine. A value of 1 creates the finest available structure, with each row representing one minimum price increment of the symbol. Higher values group multiple ticks into wider native Footprint rows.
For the Geometric and Intrabar engines, this setting is not used. Their Gaussian price distribution is calculated on the indicator’s fixed one-tick price lattice, where each row corresponds to syminfo.mintick. This keeps their modeled price-level structure at the highest available symbol resolution.
Window Bars
This setting determines how many valid candle columns the footprint table displays. Its default value is 5, and it can be set from 2 to 60.
The important point is that Window Bars does not simply mean the last N consecutive candles. Starting from the current bar, the indicator scans backward and accepts a candle only when:
the active Volume Engine provides valid volume data for that candle,
its total calculated volume is greater than zero, and
its price range overlaps the price area currently displayed by the footprint table.
If a candle does not satisfy these conditions, it is skipped and the search continues farther back until the requested number of columns is collected.
This same selection logic applies to all three engines. The practical difference is only data availability: Geometric normally has data wherever chart volume exists, Intrabar is limited by the lower-timeframe history available to the selected plan, and Footprint requires valid native Footprint data for the candle.
The search can extend up to 5,000 historical bars. Therefore, Window Bars controls the number of qualifying columns displayed, not the chronological lookback period itself.
Ticks Above/Below
This setting defines the vertical price span of the footprint table around the current price. The default value is 5, with an adjustable range from 1 to 50.
A useful way to understand this behavior is to think of an analog aircraft altimeter. The reference point remains centered while the scale moves around it. In the same way, the indicator keeps the current price on the central row while the surrounding price ladder shifts as the market moves.
The selected value determines how many one-tick rows are shown above and below that center:
Visible Rows = (2 × Ticks Above/Below) + 1
For example, a value of 5 displays 11 price rows: five above the current price, five below it, and the central row itself.
This setting applies to all three Volume Engines and controls only the visible price frame. It does not alter the underlying volume calculation or the native row grouping of the Footprint engine. Volume outside the visible frame remains outside the display and is not merged into the boundary rows.
Like an analog altimeter — the needle stays put while the dial turns behind it
Volume Concentration
This setting controls the width of the Gaussian distributions used by the indicator. Its default value is 3.0, with an adjustable range from 1.0 to 10.0 in steps of 0.5.
The relationship is:
σ = (High − Low) / Volume Concentration
Therefore, a higher value produces a smaller σ and concentrates more volume around the center of each bell. A lower value produces a larger σ and spreads the volume more broadly toward the candle’s extremes.
For the footprint table, this setting affects the Gaussian price-row construction used by the Geometric and Intrabar engines. It does not modify the native price rows when the Footprint engine is selected, because those rows are read directly from TradingView.
The same setting is also used later in the indicator’s Volume Profile construction. There, the common Gaussian profile model uses this concentration parameter regardless of which Volume Engine supplied the underlying buy and sell totals—including the Footprint engine.
Figure 1 — Higher concentration → narrower bell → more volume at the center row
Figure 2 — Concentration reshapes the row distribution, never the bar's total volume
Show Table
This setting controls whether the footprint table itself is displayed on the chart. It is enabled by default.
When Show Table is turned on, the price ladder and its candle columns are drawn according to the current footprint settings. Turning it off removes this table from the chart and provides a cleaner visual workspace.
Importantly, this option controls display only. It does not change the selected Volume Engine or the buy/sell volume values calculated by the indicator. Those volume figures remain available in TradingView’s Data Window even when the footprint table is hidden.
Table Text Size & Table Position
Table Text Size controls the font size used inside the footprint table, allowing the display to be adjusted for readability or to accommodate larger tables.
Table Position determines where the footprint table is anchored on the chart by combining its vertical and horizontal placement options.
2. Point of Control (POC)
Now that the footprint structure and the distribution of buy and sell volume across price levels have been established, we can begin examining the information that can be derived from that structure. One of the most important of these concepts is the Point of Control (POC).
What Is the Point of Control?
The Point of Control is the price level at which the greatest total volume has traded within the analyzed distribution.
At each price level, buy and sell volume are combined:
Total Volume at Price = Buy Volume + Sell Volume
The price level with the highest resulting total becomes the POC.
The importance of the POC comes from what it represents: it identifies the price where market participation was most concentrated. In other words, within the analyzed structure, this is the level where the largest amount of business was conducted and therefore where price received the greatest volume-based acceptance.
The POC should not, however, be interpreted as a directional signal by itself. It identifies where the greatest activity occurred, not whether buyers or sellers dominated that activity. Directional information must be derived from the buy/sell composition and other footprint measurements surrounding that level.
Because the POC is calculated from a defined group of price-level data, its location also depends on the period or footprint window being analyzed. As the underlying volume distribution changes, the POC can shift to a different price level.
The POC marks where business concentrated — not which side won it
How the Indicator Uses the POC
Now that the meaning of the POC is clear, we can look one level deeper and see exactly how the indicator uses it. The POC is not treated as a single decorative level; it is calculated separately for the footprint table and the chart profile, then reused in several parts of the indicator.
A. Dashboard POC
Inside the footprint table, the indicator first sums the buy and sell volume of each visible price row across all qualifying Window Bars:
Row Volume = Σ Buy Volume + Σ Sell Volume
The visible price row with the largest combined volume becomes the Dashboard POC. This means the Dashboard POC belongs specifically to the footprint window currently being analyzed, not to the entire chart history.
B. POC marker and color in the table
The selected row is marked with ● POC in the Metrics column.
Its color describes the composition of that same price level:
Buy volume greater than sell volume → Buy color
Sell volume greater than buy volume → Sell color
Equal buy and sell volume → Neutral color
The color does not redefine the POC as bullish or bearish. The POC is still selected only by total volume; the color simply shows which side contributed more volume at that level.
The row information also retains the individual Sell, Buy, and Total volumes so the user can see what produced the POC.
C. Chart POC
The POC drawn beside the Volume Profile is calculated independently from the Dashboard POC.
Across the selected profile period, the indicator evaluates the Buy and Sell profile curves at successive price samples and combines them:
Combined Profile Intensity = Buy Density + Sell Density
The sampled price where this combined profile reaches its maximum becomes the Chart POC.
This distinction is important: the Dashboard POC is derived from the footprint table's visible price rows and Window Bars, while the Chart POC belongs to the separate Volume Profile period and its sampled profile structure. They can therefore appear at different prices without either calculation being incorrect.
D. POC and profile resolution
Because the Chart POC is found from the sampled Volume Profile curve, the profile's resolution determines how finely the indicator searches for its maximum. Increasing profile resolution provides a denser set of price samples and therefore a finer localization of the Chart POC, without changing the underlying volume itself.
E. POC as the starting point of Value Area
POC also has a structural role in the next concept we will discuss: Value Area.
Rather than finding Value Area independently, the indicator begins from the POC and expands outward through neighboring price levels until the selected percentage of volume has been accumulated. In this sense, POC is not only a displayed level; it is also the anchor from which the Value Area structure is built.
F. POC settings
The Point of Control setting determines where the metric is used:
Off
Dashboard
Chart
Dashboard and Chart
The accompanying offset controls how far the Chart POC line extends beyond the Volume Profile before its label is placed. It affects presentation, not the calculated POC price.
G. POC as an output level
The indicator also publishes the Dashboard POC and Chart POC as separate level series. This allows TradingView's standard alert conditions—such as crossing, crossing up, or crossing down—to be applied to either level. We will return to this functionality in the Alerts section.
The key point is that when you see a POC in this indicator, you can identify exactly which dataset produced it, how its price was selected, what its color means, and where that level is used elsewhere in the calculation.
3. Value Area (VAH / VAL)
Once the POC identifies the price where volume is most concentrated, the next question is:
how far does that concentration extend?
The Value Area answers this by defining the price range that contains a selected share of the analyzed volume, commonly 70%.
The calculation begins at the POC and expands outward through neighboring price levels. At each step, the side containing the greater adjacent volume is added first. This process continues until the required percentage of total volume has been included.
The resulting boundaries are:
VAH — Value Area High: the upper boundary of the Value Area.
VAL — Value Area Low: the lower boundary of the Value Area.
Conceptually, the structure can be pictured as:
VAL ← accepted volume ← POC → accepted volume → VAH
The POC marks the center of greatest volume concentration, while VAH and VAL define the broader region around it where most of the analyzed trading activity took place.
Prices inside this range represent the market’s main area of volume acceptance for the selected dataset; prices outside it belong to the lower-volume remainder of the distribution.
POC sits at the peak of the combined curve; VAH and VAL mark its 70% edges
How Value Area Appears in the Indicator
The same Value Area concept is presented in two analytical contexts:
Dashboard:
The calculation uses the volume distribution of the current Footprint Window. Starting from its POC, the indicator expands toward neighboring rows until the selected volume percentage is reached. The resulting boundaries are marked directly in the Metrics column as ▲ VAH and ▼ VAL, so the accepted-volume region can be read against the footprint price ladder.
Chart:
The chart uses the Volume Profile period rather than the Footprint Window. Its own POC becomes the starting reference, and the Value Area is calculated from the profile’s volume distribution. The resulting VAH and VAL are then drawn as horizontal levels around the profile, making the high-volume acceptance range visible directly on the chart.
Because the Dashboard and Chart analyze different datasets, their POC, VAH, and VAL levels do not necessarily have to coincide.
The Value Area setting determines whether these levels are calculated and displayed in the Dashboard, Chart, Dashboard and Chart, or turned Off. The percentage beside it defines the target share of volume, with 70% as the default.
4. Volume Imbalance
Once we know where volume concentrated, the next question is whether one side became unusually dominant at a particular price. Volume Imbalance is designed to identify exactly that condition.
Unlike a simple comparison of Buy and Sell volume on the same row, footprint imbalance is measured diagonally between neighboring price levels:
Buy Imbalance:
Buy volume at a price is compared with Sell volume one row below.
Sell Imbalance:
Sell volume at a price is compared with Buy volume one row above.
With the default threshold of 300%, the dominant side must exceed the opposing diagonal volume by more than three times before the level is classified as imbalanced.
Conceptually:
Buy Imbalance:
Buy(current row) > Sell(row below) × Threshold
Sell Imbalance:
Sell(current row) > Buy(row above) × Threshold
This diagonal comparison is important because it compares opposing activity across the neighboring prices where buyers and sellers effectively meet, rather than treating each row as an isolated number.
How the Indicator Uses Imbalance
Geometric and Intrabar:
The same diagonal rule is applied to the price rows constructed by the indicator.
Footprint:
When the native Footprint engine is selected, the indicator uses TradingView's own per-row Buy and Sell imbalance results. The selected Imbalance percentage is passed directly into the native Footprint request.
Footprint Table:
An imbalanced Buy or Sell cell receives a directional marker and stronger visual emphasis, making individual imbalances visible inside each candle column.
Dashboard Metrics:
The indicator also evaluates the combined Buy and Sell distribution across the complete Footprint Window. Price levels that satisfy the diagonal imbalance rule at the window level are marked in the Metrics column, allowing isolated candle imbalances to be distinguished from imbalance that persists across the broader window.
Chart:
For the Volume Profile period, imbalance is evaluated from the profile's Buy and Sell distribution and qualifying levels are shown as dotted horizontal marks on the chart.
Imbalance Setting
The Imbalance setting enables or disables these measurements, while the percentage beside it controls how strong the diagonal dominance must be. The default is 300%; increasing it requires a stronger imbalance, while decreasing it makes the measurement more sensitive.
The important distinction is that imbalance identifies relative dominance at specific neighboring prices. It does not simply tell us which side has more total volume across the candle or profile.
Figure 1 — Buy vs sell one row below · sell vs buy one row above · threshold 300%
Figure 2 — A buy imbalance needs the buy row to beat the sell row below it by the threshold
Figure 3 — Sell(P) > Buy(P + 1) × threshold · the buy rule flipped upside down
5. Delta and Totals
After identifying where volume is concentrated and where local imbalances appear, the same Buy/Sell data can be summarized through two direct measurements: Total Volume and Delta.
Total Volume shows how much activity occurred:
Total = Buy Volume + Sell Volume
Delta shows the difference between the two sides:
Delta = Buy Volume − Sell Volume
A positive Delta means Buy volume was greater, while a negative Delta means Sell volume was greater. A value near zero indicates that the two sides contributed similar amounts of volume.
The important distinction is that Delta measures volume dominance, not price direction. A candle can rise while having negative Delta, or fall while having positive Delta, because price movement and Buy/Sell volume balance describe different aspects of market activity.
How They Appear in the Dashboard
At the bottom of each Footprint candle column, the indicator displays:
Total: the complete Buy + Sell volume of that candle.
Delta: the Buy − Sell difference for that candle.
Positive Delta is displayed in the Buy color and negative Delta in the Sell color, while Total remains neutral.
The Sum column applies the same calculation to the entire Footprint Window. It combines the Buy and Sell totals of all qualifying Window Bars and reports:
Window Total = Σ Buy + Σ Sell
Window Delta = Σ Buy − Σ Sell
This allows the user to compare the balance of an individual candle with the broader balance of the complete Footprint Window.
Current-Bar Summary
The status bar at the top of the table also provides a live summary of the current candle, including its Total Volume, Sell Volume, Buy Volume, and the side currently leading.
The lead percentage measures how large the Buy/Sell difference is relative to the candle's total volume:
Lead % = |Buy − Sell| / Total × 100
This makes it possible to distinguish between a small numerical advantage and a genuinely meaningful difference in participation.
The background color of this status bar follows the candle's price change, not its Delta. Therefore, price direction and volume dominance remain intentionally separate.
On the Chart
Total and Delta are not drawn as independent horizontal levels on the chart. Instead, their underlying Buy and Sell volumes continue into the Volume Profile calculations, where the distribution and broader balance between the two sides are analyzed in greater detail.
So while Imbalance asks where one side became unusually dominant at specific neighboring prices, Delta answers a broader question:
Which side carried more volume over this candle or window, and by how much?
6. The Volume Profile
The Footprint shows volume inside individual candles. The Volume Profile takes the next step and combines that information across a selected period to show where Buy and Sell activity accumulated through price.
Instead of one conventional profile, the indicator builds two separate distributions:
Buy Profile
Sell Profile
For every bar in the selected period, its Buy and Sell totals are distributed through that bar’s High–Low range using the same Gaussian framework introduced earlier. These individual distributions are then added together to create the two final profile curves.
An important distinction is that this common profile model is used for all three Volume Engines. Even when the Footprint engine is selected, the chart profile is constructed from its bar-level Buy and Sell totals rather than directly reproducing TradingView’s native footprint rows. This gives all engines the same mathematical basis for profile comparison.
How It Appears on the Chart
The profile spans from the lowest Low to the highest High of the selected period and is drawn to the right of the latest candle.
Both curves grow from the same baseline and use the same horizontal scale, so their relative widths remain meaningful:
Wider Buy curve → greater Buy intensity at that price
Wider Sell curve → greater Sell intensity at that price
Where the two filled curves overlap, the chart also makes the shared price territory visually apparent.
This profile becomes the foundation for several measurements already introduced or still to come, including Chart POC, VAH/VAL, profile Imbalances, OVL, Balance Tilt, and the Residual self-check.
Main Profile Settings
Profile Period: number of bars included in the calculation.
Profile Width: horizontal drawing size of the profile.
Profile Resolution: number of price samples used to trace the curves; higher values produce finer visual resolution without changing the underlying volume model.
Buy / Sell visibility, Fill and Colors: control presentation only.
In short, the Footprint answers what happened inside each candle, while the Volume Profile reorganizes the same Buy/Sell information around price across an entire period.
Same horizontal scale on both sides — so relative widths are directly comparable
7. Overlapping Coefficient (OVL) and Balance Tilt
The Buy and Sell profiles tell us where each side was active. The next question is:
how much did they operate at the same prices?
The Overlapping Coefficient (OVL) measures this shared price territory.
Before comparison, the Buy and Sell profiles are normalized so that differences in total volume do not distort their shapes. The indicator then measures the common area between the two distributions.
Conceptually:
OVL = Shared Area of the Normalized Buy and Sell Profiles
Its range is:
0.00 → almost no overlap
1.00 → complete overlap
A high OVL means Buy and Sell activity occurred largely at the same prices. A low OVL means the two sides occupied increasingly different price regions.
How the Indicator Interprets OVL
The indicator uses 0.75 as its balance boundary:
OVL ≥ 0.75 → BALANCED
OVL < 0.75 → OFF BALANCE
But OVL alone tells us only that the distributions have separated; it does not tell us which side carried more total volume. For that, the indicator uses Balance Tilt:
Balance Tilt = (Buy Volume − Sell Volume) / Total Volume × 100
Positive values lean toward Buy, while negative values lean toward Sell.
The Balance Tilt % setting determines how large this difference must be before a direction is named. Its default is 5%.
Therefore, when OVL is below 0.75, the chart can report:
OFF BALANCE TO BUY
OFF BALANCE TO SELL
OFF BALANCE — when the volume difference is too small to assign a direction confidently.
On the Chart
The OVL reading appears at the top of the Volume Profile and reports both the coefficient and its interpretation.
This gives two complementary pieces of information:
OVL → Are Buy and Sell occurring at the same prices?
Tilt → If not, which side carries more total volume?
OVL: how much do buy and sell share the same prices?
Balance Tilt: which side carried more total volume
Two questions, answered in order
OVL asks how much the sides share the same prices; Tilt names the heavier side only when OVL says off balance
OVL measures shared prices · Tilt classifies direction when off balance · together they read the profile's balance
8. Residual Self-Check (RES)
Because the Volume Profile is built mathematically, the indicator also verifies that its own reconstruction has not created or lost meaningful volume.
For each side, it compares the original Buy or Sell total with the volume recovered from the complete profile model—including the Gaussian curves and any zero-range bars represented as point masses.
Conceptually:
Residual = Difference between original volume and reconstructed profile volume
The Buy and Sell errors are combined and expressed in parts per million (PPM). A value close to zero means the profile reproduces its source volume with extremely small numerical error.
How It Appears on the Chart
The RES reading is displayed beneath the Volume Profile:
EXACT — Residual is within the permitted tolerance.
DRIFT — Residual exceeds that tolerance.
The default Residual Tolerance is 1 PPM. Lower values make the self-check stricter; higher values allow more numerical deviation.
RES is therefore not a market signal. It is a diagnostic of the indicator itself: a confirmation that the profile you are analyzing remains mathematically consistent with the volume from which it was constructed.
9. Remaining Settings
Most analytical settings have already been explained alongside the concepts they control. Only a few display-related controls remain:
Balance Tilt % sets the minimum Buy/Sell volume difference required before an off-balance profile is labeled toward Buy or Sell. The default is 5%.
Residual Tolerance PPM defines the maximum RES value still classified as EXACT. The default is 1 PPM.
Label Merge Ticks controls how close chart labels may be before they are combined to prevent overlap. A value of 0 keeps nearby labels separate.
Frame Lines control only the color, thickness, and style of the Volume Profile boundary lines.
The remaining profile visibility, colors, fill opacity, width, and resolution settings affect presentation only and were introduced with the Volume Profile itself.
10. Alerts
The indicator does not impose a predefined trading signal. Instead, it publishes its two POC levels as separate series so they can be used directly inside TradingView’s standard Alert dialog:
Dashboard POC — the POC calculated from the current Footprint Window.
Chart POC — the POC calculated from the Volume Profile period.
After selecting either series as the alert source, TradingView’s normal conditions can be applied, including actions such as:
Crossing · Crossing Up · Crossing Down · Greater Than · Less Than
This keeps the alert logic flexible. The indicator provides the calculated level; the user decides what interaction between price and that level is important for the strategy being monitored.
For example:
Price crosses above Chart POC → Crossing Up
Price crosses below Dashboard POC → Crossing Down
Dashboard POC and Chart POC remain independent, so an alert can be built around either the short footprint window or the broader profile structure.
Publisher’s Note
Volume is one of the fundamental dimensions of market analysis because it reveals the level of participation behind price and helps us understand where trading activity is taking place.
This indicator was created first to make Footprint concepts accessible across TradingView plans. The Geometric and Intrabar engines provide approximate Buy/Sell representations, while the Footprint engine uses TradingView’s official native price-level data where available.
An important part of the project was to place these different data sources inside a common mathematical framework. TradingView’s native Footprint presents its price-level structure directly as footprint rows, while this indicator also uses Gaussian mathematics and price geometry to construct a comparable Buy/Sell Volume Profile from the bar-level totals of all three engines. In this way, different sources can be examined through the same analytical language rather than through unrelated visual forms.
The purpose is not to claim that an approximation is equivalent to native data. It is to make the differences measurable, understandable, and comparable. For users who enjoy investigating how markets are represented—and how different methods arrive at their results—the three engines can themselves become an interesting field of study.
Ultimately, the goal is simple: to move from merely seeing volume to understanding its structure. مؤشر
Futures Volume + Delta - CFD ChartsA volume pane for CFD and cash-index charts that shows the REAL traded volume
of the matching futures contract — plus an estimated buy/sell delta and
absorption flags for effort-without-result bars.
What makes it original: CFD "volume" is broker tick count, not market
participation. This pane never uses it — every column is the exchange volume
of the auto-detected futures contract. The delta column is built from the
futures contract's own lower-timeframe candles (up-candle volume counts as
buying, down-candle volume as selling): the volume is real, only the side
attribution is an estimate, and it is labeled as such. Absorption flags then
combine both axes — volume percentile high while the price range percentile is
low — the candle-data footprint that hidden passive interest (iceberg-style
execution) leaves behind. Pine has no order book, so this is explicitly a
footprint proxy, not order-book detection.
How it works:
- The futures contract is auto-detected from the chart symbol (DAX/GER40 ->
FDAX, NAS100 -> NQ, US30 -> YM, UK100 -> Z, US500 -> ES), or set manually.
- Histogram = futures volume per chart bar; columns tint with bar direction,
a configurable MA marks the average.
- Delta = buy-minus-sell futures volume from 1/5/15-minute intrabars
(auto-selected by chart timeframe, manual override). Lower-timeframe
history is limited, so the delta reaches less far back than the histogram.
- Absorption flag (orange diamond + alert): volume percentile >= X and range
percentile <= Y over a rolling lookback — both thresholds adjustable.
- A status label confirms the active source and the delta resolution.
How to use it: read it like a footprint-lite. Rising price on rising futures
volume = participation confirms the move. An absorption diamond after an
extended run — heavy contracts traded, no price progress — marks where passive
interest is absorbing the aggression; combined with a one-sided delta it is a
common exhaustion/iceberg footprint. Delayed futures feeds confirm bars a few
minutes late; the historical picture is complete.
*This script is part of a consistent set of open-source session, range and
volume tools — the companions are on my profile.*
مؤشر
Orderflow Suite [martineye15]Orderflow Suite — four order-flow tools in one indicator: Cumulative Volume Delta, footprint bars, imbalance / absorption signals, and a volume profile. Each module toggles independently, so you can run the full suite or just the part you need. CVD gets its own pane; the footprint, signals and profile draw directly on the price chart, so no second script is required.
MODULES
- Cumulative Volume Delta (CVD): running buy-minus-sell volume, with Session / Day / Week / None reset anchoring and a Line, Columns or Candle display (candles show open = previous CVD, close = new CVD, wicks from the intrabar delta extremes). Optional price-CVD divergence: bearish when price makes a higher high while CVD makes a lower high, bullish when price makes a lower low while CVD makes a higher low.
- Footprint bars: the most recent bars are split into price bins, each showing aggregated buy x sell volume, shaded by its net delta, with the bar's highest-volume bin (VPOC) framed.
- Imbalance & absorption: delta-imbalance triangles when |delta| / volume passes a threshold, stacked-imbalance zones when several same-direction imbalance bars line up, plus absorption labels (heavy volume in a tight range near a swing) and exhaustion labels (a new swing high on negative delta, or a new swing low on positive delta).
- Volume profile: a volume-at-price histogram over a lookback window with POC, value-area high / low and the 70% value area, in total-volume or delta-coloured mode.
DELTA ENGINE (please read)
TradingView does not provide a true bid/ask tick feed, so delta here is an approximation, not exchange order flow. Historical delta is estimated from lower-timeframe intrabars: an intrabar counts as buy volume when it closes above its open, sell volume when below, and is split evenly on an unchanged close. On the live bar you can optionally accumulate tick-based delta instead (uptick = buy, downtick = sell). Because TradingView does not store ticks, realtime values built this way can differ from what the same bar shows after a chart refresh. Treat every delta value as an estimate.
ALERTS
Ten conditions: CVD bullish / bearish divergence, bullish / bearish imbalance, stacked bullish / bearish imbalance, absorption at highs / lows, and exhaustion top / bottom.
HOW TO USE
Add it to a symbol that has volume (crypto, futures or stocks — spot forex usually has no real volume, and the tool will tell you so). Intraday timeframes from about 1 minute to 1 hour work best. Turn on the modules you want, set the delta engine (lower-timeframe auto / manual, and optional realtime tick mode), and adjust the per-module thresholds and sizes. Use CVD and its divergences for momentum and non-confirmation, the footprint and profile to see where volume actually traded, and the imbalance / absorption / exhaustion signals as context around swings. Set alerts on any of the ten conditions.
WHAT MAKES IT DIFFERENT
It combines CVD, footprint, imbalance / absorption and a volume profile in a single indicator, sharing one delta engine and drawing the price-chart modules through force_overlay from a lower pane — a combined order-flow view without stacking several scripts. Drawing counts are budgeted internally so the modules together stay within TradingView's object limits.
REPAINTING & LIMITATIONS
Confirmed-bar behaviour is stable: footprints are built on closed bars, CVD divergences use confirmed pivots (so they appear a few bars after the pivot — normal pivot lag, not repainting), and the profile is computed over completed bars. The delta approximation is the main caveat: the live bar's delta is an estimate, and if you enable realtime tick mode, the live values will not match the same bar's historical lower-timeframe values after a refresh — this is inherent to how TradingView exposes data and is noted in the input tooltips. One-second intrabars need a plan with seconds data; without it the tool uses a one-minute fallback, and very old bars beyond the intrabar budget fall back to whole-bar classification. A symbol with no volume cannot produce delta and will show a notice instead.
This is a visual, decision-support tool. It is not a strategy, it places no orders and reports no performance statistics, and it is not financial advice. مؤشر
Footprint Master Pane [ZynAlgo]Overview
ZynAlgo Footprint Master Pane is an order-flow and footprint-style volume analysis indicator designed to help traders study micro-liquidity behavior inside each candlestick.
The tool displays intrabar volume-side activity as a matrix-style Footprint Profile in a separate pane. Instead of only observing open, high, low, and close movement, traders can study where buying and selling pressure appears across different price levels inside recent candles.
This can help users evaluate:
Intrabar volume concentration
Buy-side and sell-side pressure
Delta behavior inside each candle
Point of Control placement
Liquidity concentration at candle highs and lows
Potential absorption or exhaustion behavior
Chart example:
How to Read the Footprint Matrix
Each data block on the chart is displayed in this format:
Bid | Ask
Price Level
The price level represented by that row of the footprint matrix.
Bid - Left Number
Represents sell-side volume activity classified by the script for that price level.
Ask - Right Number
Represents buy-side volume activity classified by the script for that price level.
Background Color - Heatmap
The higher the volume at a price level, the stronger the heatmap intensity.
Default color logic:
Cyan: buy-side activity is dominant at that price level.
Pink: sell-side activity is dominant at that price level.
Gray: low or inactive liquidity area.
Key Highlights
POC - Point of Control
The Point of Control is the price level with the highest total trading volume within the selected candlestick.
It is displayed as the gold zone and represents the main volume concentration area for that candle.
Footer Metrics
The bottom of each footprint column displays summary data for the candle.
Delta
Delta represents the net difference between buy-side and sell-side volume activity.
A positive delta indicates that buy-side pressure is dominant. A negative delta indicates that sell-side pressure is dominant.
Total Volume
Total volume represents the combined volume activity for the entire candlestick.
Configuration Settings
1. Order Flow Engine
Intrabar Timeframe
Defines the lower timeframe used by the tool to extract intrabar volume information.
Lower intrabar timeframes can provide more detailed footprint construction, while higher intrabar timeframes may produce a smoother and lighter display.
Stack Levels - Height
Controls how many price levels each candlestick is divided into.
Higher values:
Show more footprint detail
Create a finer price-level breakdown
May increase chart processing load
Lower values:
Create a simpler footprint view
Reduce visual density
May run more smoothly on slower charts
Auto Detect Asset - Smart Grid
When enabled, the system attempts to measure the current asset's volatility and calculate an appropriate grid size automatically.
This is useful when switching between markets such as gold, crypto, forex, indices, or stocks.
Manual Tick Size
When Auto Detect Asset is disabled, users can manually define the tick or grid size.
This can be useful when a symbol requires a custom footprint scale.
2. Pane Visuals
Recent Bars to Render
Controls how many recent candles are displayed in detailed footprint form.
Limiting the number of rendered candles can help keep the chart responsive on TradingView.
Color Customization
Users can customize the colors for:
Buy-side activity
Sell-side activity
Point of Control zone
Heatmap display
This allows the footprint pane to match different chart themes and visual preferences.
Basic Analytical Applications
1. Absorption Observation
When price approaches a key support or resistance area, footprint data can help traders study whether one side of the market is being absorbed.
For example, if a bearish candle shows positive delta and large volume near the lower part of the candle, it may suggest that sell pressure is being absorbed by buy-side participation.
2. POC Migration
Point of Control migration can help traders evaluate where value is shifting across consecutive candles.
In an uptrend, POC zones that continue to migrate higher may suggest that market participation is accepting higher prices.
3. Liquidity at Highs and Lows
Traders can inspect volume activity near candle highs and lows to study exhaustion behavior.
For example, if price reaches a new high but the top levels show very low participation, that may indicate weaker continuation pressure.
How to Use
Add the indicator to the chart.
Choose an intrabar timeframe suitable for the chart timeframe and market.
Adjust stack levels to control footprint detail.
Use the heatmap to identify price levels with stronger participation.
Monitor the POC to study where volume concentration forms inside each candle.
Compare delta and total volume to evaluate buy-side or sell-side pressure.
Combine footprint observations with market structure, support and resistance, liquidity zones, and risk planning.
Best Use Cases
This indicator may be useful for:
Order-flow style analysis
Footprint chart reading
Intrabar volume analysis
Delta observation
Point of Control tracking
Absorption study
Exhaustion analysis
Liquidity-zone confirmation
Limitations
Footprint values depend on the intrabar data available from TradingView for the selected symbol and timeframe.
The Bid and Ask display is based on the script's volume-side classification logic and should be interpreted as analytical volume-side data.
Lower intrabar timeframes may provide more detail but can increase processing load.
A footprint imbalance does not guarantee price continuation or reversal.
The indicator does not provide automatic trade entries, exits, or position management.
Past order-flow or footprint behavior does not guarantee future results.
Important Note
This indicator is an analysis tool only. It does not provide financial advice, investment advice, or guaranteed trading results. Users are responsible for their own trading decisions and risk management.
مؤشر
NEXUS Three professional modules. One unified indicator.🌐 WHAT IS NEXUS?
NEXUS combines three independent professional-grade
modules into a single, clean indicator — eliminating
the need to stack multiple scripts on your chart.
- MODULE 1 — Market Sessions Visualizer
- MODULE 2 — Volume–Price Divergence Detector
- MODULE 3 — Key Levels & Structural Reference Engine
━━━━━━━━━━━━━━━━━━━━━
MODULE 1 — MARKET SESSIONS
━━━━━━━━━━━━━━━━━━━━━
Visualize the four major Forex/Futures trading sessions
with precision and full customization.
★ Sessions covered:
· Tokyo (default: 00:00–09:00 UTC)
· London (default: 07:00–16:00 UTC)
· New York (default: 13:00–22:00 UTC)
· Sydney (default: 21:00–06:00 UTC)
★ Display modes:
· Boxes — classic high/low range boxes
· Zones — colored background highlights
· Timeline — compact bar-level session markers
· Candles — colored candle bodies per session
★ Additional tools:
· Session Open/Close line
· Session 0.5 midpoint level
· Merge overlapping sessions
· Live pip / % change labels
· Weekend exclusion toggle
· Fully customizable colors, borders & label sizes
· Lookback depth control (default: 150 days)
· All session times entered in UTC — no timezone
conflicts regardless of your chart setting
★ Alerts:
Session open · New session high · New session low
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MODULE 2 — VOLUME–PRICE DIVERGENCE
━━━━━━━━━━━━━━━━━━━━━
Identifies moments when volume surges significantly
but price barely moves — a classic footprint of
institutional accumulation or distribution.
★ Two signal types:
· 🔴 DIV (Divergence) — high volume + small price body
→ Strong sign of hidden positioning
· 🟠 SHADOW — high volume + large wick / shadow candle
→ Rejection or absorption signal
★ How it works:
· Compares current volume against a 20-bar SMA
· Volume spike threshold: configurable (default 600%)
· Price change measured via 3 selectable methods:
— Body / Open
— Body / Range
— Range / Open
· Shadow detection with adjustable body-to-wick ratio
★ Visual aids:
· Color-coded volume histogram
(Gray → Yellow → Orange → Red)
· On-chart signal shapes with text labels
· Live info table (top-left):
current volume · volume % change · price % change
body/range ratio · current signal status
★ Alerts:
Volume spike · Shadow candle · Full divergence signal
━━━━━━━━━━━━━━━━━━━━━
MODULE 3 — KEY LEVELS & STRUCTURAL REFERENCE
━━━━━━━━━━━━━━━━━━━━━
A comprehensive reference engine that plots all
critical price levels a professional trader monitors.
★ Session High/Low levels:
· Asia Session (18:00–03:00 ET, configurable)
· London Session (03:00–08:00 ET, configurable)
· New York Session (08:30–17:00 ET, configurable)
→ Lines auto-update in real time
→ Auto-detect and label when a level is broken
★ Market structure levels (ET — NYSE aligned):
· Key time opens: 00:00 · 02:00 · 05:00 · 08:30
09:30 · 10:00 · 14:00 · 15:00 · 16:00 · 17:00
· 4H Open · Daily Open · Weekly Open · Monthly Open
★ Range midpoint levels:
· Previous Day Mid Range (50%)
· Market Open Mid Range (live, updates intraday)
· Weekly Mid Range
· Monthly Mid Range
★ Higher-timeframe levels:
· Previous Day High / Low
· Weekly High / Low
· Monthly High / Low
· All-Time High
★ Pivot calculations (previous day data):
· Standard Pivot Points: PP · R1 · R2 · S1 · S2
· Central Pivot Range (CPR): TC · PP · BC
with CPR width % context label (Trending / Mixed / Range)
★ Smart features:
· Auto-Simplify on HTF — automatically hides granular
levels on higher timeframes to reduce visual noise
· Smart label positioning that adapts to screen view
· Level confluence detection — merges labels when
two levels share the same price
· Vertical day separator lines at 17:00 ET (last 14 days)
· Days-of-week labels at session open
· Extend All Lines toggle
· Global color & text size override
· Full per-level style control (color · style · width)
· Timezone selector for all session calculations
━━━━━━━━━━━━━━━━━━━━━
⚙️ QUICK SETUP GUIDE
━━━━━━━━━━━━━━━━━━━━━
1. Add NEXUS to any intraday chart (1m to 4H recommended)
2. Sessions Module
→ All session times are in UTC by default
→ No need to change your chart timezone
→ Toggle sessions on/off individually
→ Choose your preferred display mode
3. Divergence Module
→ Start with default settings
→ Lower the Volume Threshold (%) on low-liquidity
instruments if signals are too rare
→ Enable "Show Debug Info" for manual calibration
4. Key Levels Module
→ Set your timezone (default: America/New_York)
→ Toggle each level group independently
→ Enable "Auto-Simplify on Higher Timeframes" to keep
the chart clean when zooming out
→ Use "Extend All Lines" for a forward-looking view
━━━━━━━━━━━━━━━━━━━━━
📌 RECOMMENDED TIMEFRAMES
━━━━━━━━━━━━━━━━━━━━━
All modules active → 1m · 3m · 5m · 15m · 30m
Sessions + Levels → 1H · 2H · 4H
Levels only → Daily
⚠️ Sessions module is intraday-only by design.
It will not plot on Daily or higher timeframes.
━━━━━━━━━━━━━━━━━━━━━
📊 COMPATIBLE INSTRUMENTS
━━━━━━━━━━━━━━━━━━━━━
Futures (ES, NQ, MES, MNQ, YM, CL, GC...)
Forex (all major, minor & exotic pairs)
Crypto (BTC, ETH and all altcoins with volume)
Equities (US stocks & ETFs with intraday data)
Indices (SPX, NDX, DXY...)
━━━━━━━━━━━━━━━━━━━━━
📋 RELEASE NOTES
━━━━━━━━━━━━━━━━━━━━━
v1.0 — Initial release
· Full integration of all three modules
· Smart label positioning engine
· Timeframe-aware auto-simplification
· Confluence level merging
· Complete alert system
━━━━━━━━━━━━━━━━━━━━━
⚠️ DISCLAIMER
━━━━━━━━━━━━━━━━━━━━━
This indicator is for informational and educational
purposes only. It does not constitute financial advice.
Past signals do not guarantee future results.
Always manage risk appropriately. مؤشر
Multi Timeframe Order-Flow CockpitMulti-Timeframe Order-Flow Cockpit
A single overlay that turns raw volume into a multi-timeframe order-flow read. It draws a compact row of higher-timeframe (HTF) candles beside price — each split into buy vs sell volume — and condenses several timeframes' order flow into one plain-language verdict. Unusually, it ships with a built-in calibration layer that forward-tests its own verdict and tells you, in plain words, whether that verdict actually beats a base rate — instead of asserting an edge it cannot show.
WHAT IT DOES
For each timeframe you enable, the script reconstructs the buy/sell volume that built every bar, scores how one-sided that flow is, reads market structure, and fuses everything into a single bias with a stated conviction. It then projects key price levels and marks where current price sits inside each timeframe's range — so you can see, at one glance, what order flow is doing across the whole timeframe stack.
WHY THESE COMPONENTS ARE COMBINED
This is not a pile of unrelated overlays. It is a pipeline where each stage feeds the next, and a final stage audits the whole:
Higher-timeframe candles are the canvas — see several timeframes' auctions at once without flipping charts.
Intrabar delta classifies sub-bar volume into buy vs sell (close-vs-open, or range-weighted). Where your plan and symbol allow, the chart-timeframe strip uses native footprint (real aggressor bid/ask volume), tagged REAL; otherwise it reconstructs the delta and is tagged EST — so data fidelity is never hidden.
Order-flow toxicity (VPIN-family) measures how aggressive/one-sided each timeframe's flow is. It is a conviction input, not a direction.
Market structure (swing breaks, BOS/CHoCH) supplies directional context that can override delta when price structure genuinely shifts.
Probabilistic synthesis fuses the above in log-odds space, averaging correlated inputs so that redundant agreement cannot inflate confidence, plus an optional exogenous volatility-index vote (rising volatility pressures the asset).
Forward calibration resolves every committed verdict N bars later against an ATR-sized move and reports Hit% versus an unconditional Base%, gated by a Wilson score interval so a result is only flagged (star) when it is statistically distinguishable from chance — split by trending versus ranging regime.
The components belong together because the deliverable is the synthesis and its honest scoring, which no single overlay can provide: toxicity needs the delta, structure contextualizes the delta, the verdict needs all of them, and the calibration is meaningless without a verdict to test.
HOW TO USE IT
Read the Compact table (default). Bias = which way flow leans; Conviction = how strongly; Flow = calm vs one-sided; Setup = exhaustion flags; Edge = whether the verdict has measured, significant edge; Use = the bottom line — "Context only" until edge is proven, "Tradeable — still verify" only when the calibration confirms it.
Switch the table to Pro for per-timeframe metrics (delta %, toxicity, structure, calibrated bias) plus the combined verdict and calibration rows. Turn on the Key Info table while learning — it is a plain-language legend.
On-chart aids: each panel tags its true High/Low and shows a position triangle at its edge — green when price is above that timeframe's range, red when below, yellow when inside, with brightness scaling to how far price has stretched. Key levels (POC, value-area high/low, recent swing high/low) are projected at true price as reference zones.
Any market: set your symbol, price source and intrabar resolution in Data Source, and your market's volatility index in Volatility Vote. Defaults are set for NSE NIFTY — change them for other assets.
Alerts are attention cues (verdict shift, toxicity spike, divergence, structure shift) — they tell you where to look, never what to do. Create one alert with the condition "Any alert() function call."
WHAT IS ORIGINAL HERE
Most order-flow and "smart-money" tools assert an edge by drawing confident signals. This one is built around the opposite discipline: a transparent calibration layer that measures and reports its own edge against a base rate, refuses to certify a signal that has not earned statistical significance, and reports it separately by market regime. The multi-timeframe order-flow synthesis with decorrelated log-odds fusion, and the REAL/EST fidelity tagging of delta, are also original to this implementation. The honesty layer is the headline feature — it is designed to tell you when there is no proven edge.
CONCEPT CREDITS (methods, not code)
Bulk Volume Classification / VPIN — Easley, Lopez de Prado and O'Hara. Market/Volume Profile (value area, POC) — J. Peter Steidlmayer. Wilson score interval — E. B. Wilson (1927). Efficiency-ratio regime — Perry Kaufman. Market-structure concepts — classical price action. All original code, architecture, fusion and calibration by the author.
DISCLAIMER
This is an awareness/context tool — not a signal generator and not financial advice. No indicator predicts the future. Past behavior and any displayed statistics do not guarantee future results; the calibration is explicitly designed to report when the tool has no measurable edge — heed it. Trade your own plan and manage your risk. مؤشر
Order Flow Footprint [JOAT]ORDER FLOW FOOTPRINT
A full intrabar footprint engine — POC, Value Area, per-row imbalance detection, stacked imbalance zones, delta-flip alerts, POC-shift alerts, and per-bar footprint ladder labels — built to read like a Bookmap-style auction view directly on a TradingView chart. Uses the native Footprint API when your plan provides it, with a graceful fallback to lower-timeframe tick-rule reconstruction so the script works on every account tier.
Data source — Footprint API or reconstructed
Three modes, behaviourally identical:
Footprint API — uses request.footprint() with a configurable aggregation (Auto / 1m / 3m / 5m / 15m / 30m). Native bid/ask volume per row when your plan supports it.
Reconstructed — uses request.security_lower_tf() to sample intrabar prints, then assigns each tick to buy or sell via the standard tick rule. Configurable LTF (1m / 3m / 5m / 15m / 30m).
Auto — picks Footprint when available, falls back to Reconstructed. The recommended default.
A configurable Profile Rows input (default 24, max 80) sets the vertical resolution — each chart bar's intrabar volume is bucketed into N horizontal price slices and the footprint is built from those buckets.
Imbalance detection (Bookmap diagonal)
The institutional definition of an imbalance is diagonal , not lateral :
A buy row is imbalanced when buy ≥ ratio × sell (this row's buys vs the row below it's sells).
A sell row is imbalanced when sell ≥ ratio × buy (this row's sells vs the row above it's buys).
The ratio is configurable (default 3.0× — the institutional norm). An optional minimum-row-volume filter mutes illiquid wick rows from being counted as imbalances. When N or more same-side imbalances occur inside a single bar, the Burst alert fires.
Stacked imbalance zones (the headline)
Stacked imbalances are the textbook order-flow structural read: N consecutive same-side imbalanced rows on top of each other. They mark where price had to gap through multiple thin levels to print — and they are reliable revisit zones.
Minimum stack count is configurable (default 4 consecutive rows).
Each stack zone is drawn as a box extending right by a configurable bar count.
Optional glow border on stack zones (toggleable).
Maximum active zones is capped (default 8) — older zones shift out FIFO.
POC + Value Area
Per-bar Point of Control (the row with the highest volume), Value Area High and Value Area Low (the 70%-volume centred range) are computed and toggleable. POC is rendered as a dot; VAH / VAL as lines. A configurable POC Shift Threshold (in mintick units) fires the POC Shift alert when the POC jumps more than the threshold bar-over-bar.
Delta flip detection
Per-bar delta (buy − sell) is computed continuously. The delta-flip alert fires only when delta switches sign after at least N consecutive same-sign bars (configurable, default 5). This filters out the micro-fluctuations that pure-sign-flip detectors would noise on.
Per-bar footprint ladder (optional, heavy)
When enabled, inline labels at every row of every recent bar (configurable history depth) show the buy × sell tuple per row — the full Bookmap-style read. This is visually rich but rendering-heavy; keep it off when scrolling long histories or running on low-end hardware.
Visual system
POC dot in accent yellow.
Value Area lines (VAH / VAL).
Imbalance cells highlighted in palette colour per row.
Stacked imbalance zones with optional glow.
Bar colouring by delta sign (toggleable).
Per-bar delta annotations every N bars (configurable).
Per-bar ladder labels (optional).
A locked Iridescent palette (magenta bull / cyan bear / yellow POC accent on pure black) gives the chart a cyberpunk holographic identity that reads as institutional rather than retail.
Dashboard
Monospaced table positionable to any of nine corners. Surfaces:
Source mode in use (Footprint / Reconstructed) with aggregation/LTF.
Current bar's delta value and sign.
POC price and intrabar volume share.
Imbalance count this bar (buy / sell).
Active stack zone count.
Last delta-flip direction with bar-age.
Alerts
Five alert conditions, each independently controllable:
Stacked Imbalance Zone Formed
POC Shift (POC jumped > threshold)
Delta Flip After Run (delta sign change after N+ same-sign bars)
Buy Imbalance Burst (N+ buy imbalances in one bar)
Sell Imbalance Burst (N+ sell imbalances in one bar)
How to read it
Three reads, in order of conviction:
Stacked imbalance zone — the highest-conviction read. Price had to plough through multiple thin levels to print. Future revisits are reliable reactive zones.
Delta flip after run — regime-change signal. When delta has been net-buying for many bars and finally flips net-selling, the auction direction has just rotated.
POC shift through a structural level — value migration. When the POC moves through a previous-day VAH or VAL, value has migrated and the next session's bias often follows.
Suggested settings
Defaults (1m reconstruction LTF, 24 rows, 3.0× imbalance ratio, 4-stack minimum) are tuned for 5m–15m charts on liquid markets. For 1m scalping, drop rows to 16 and stack minimum to 3. For HTF macro (1H+), raise rows to 36 and stack minimum to 5. The 3.0× imbalance ratio is the institutional Bookmap norm; tighten to 2.5× for more frequent imbalance prints.
Originality
The implementation — the Auto/Footprint/Reconstructed source router, the diagonal-Bookmap imbalance detector with row-volume filter, the consecutive-row stack zone builder with glow border, the POC-shift detector with mintick-unit threshold, the run-length-gated delta-flip alert, the per-bar ladder renderer with history depth control, and the dashboard's holographic palette — is JOAT-original. No third-party code reused. The footprint vocabulary (POC, VAH/VAL, imbalance, stacked imbalance, delta) is public-domain auction-theory language; the implementation here is purpose-built for chart-based Pine v6.
Limitations
Reconstructed footprints are an approximation — the tick rule is the accepted public-market inference for assigning intrabar trades to buy/sell but it is not a direct read of bid/ask volume. Footprint API requires a TradingView Premium or Ultimate plan and is unavailable on some instruments. The per-bar ladder labels are rendering-heavy; keep them off when historical performance matters. Stacked-imbalance zones honour the max active cap (FIFO eviction) and the extension bars cap.
—
-made with passion by jackofalltrades
مؤشر
Order-Flow Profile Microstructure & Calibrated SweepsOrder-Flow Profile — Footprint, Microstructure & Calibrated Sweeps
A single-pane volume profile that reconstructs intrabar buy/sell activity, renders it as a footprint / delta heatmap with Point of Control and a 70% Value Area, layers a stack of market-microstructure factors over the same price bins, and then forward-tests every reversal signal it emits against realized outcomes. The dashboard reports measured edge with confidence intervals — not asserted edge.
It runs on any liquid symbol and any intraday timeframe. Defaults are tuned for index futures (e.g. NIFTY / BANKNIFTY); a few inputs adapt it to other instruments.
What it plots
Order-flow profile drawn to the right of price: each price bin colored by who controlled it (delta) with brightness scaled to volume, or a classic split footprint. The peak-volume row is the POC; a 70% Value Area is built outward from the POC.
Low-volume nodes / voids and four quadrant deltas that localize where buying and selling concentrated within the range.
A VPIN heat-glow background whose brightness rises with flow toxicity.
Sweep tags (ABS / EXH / DIV / REJ) at liquidity extremes, and a ⚡ reclaim-confirmed liquidity-sweep marker for the high-conviction stop-run-and-reclaim subset.
A calibration / key-reads dashboard (Compact by default, Pro on demand) that adapts its colors to your chart's background luminance.
Why these components belong in one script (component rationale)
This is not a bundle of unrelated indicators stacked together. Every component describes one object — the order-flow auction taking place inside the price profile — and each measures a different facet of it. They share one substrate (the price bins) and one validation spine (the calibration engine):
The profile says WHERE volume traded. POC, Value Area and voids are the structural skeleton — the price levels that matter.
Trade classification splits that volume into buy vs sell, giving every bin a delta. Bulk Volume Classification (a Student-t CDF on the standardized intrabar move) is used by default; on Premium plans, native bid/ask footprint can replace it, feeding the same bins. A tick rule classifies the same intrabars in parallel and the agreement % is reported, so you know when the trade-side read is fragile.
The microstructure factors qualify HOW that flow behaves at those levels. VPIN (informed vs balanced), multi-level OFI (depth-weighted imbalance across the bins), Kyle's λ and Amihud (price impact / illiquidity), and √-law absorption (flow soaked up vs fragile) each answer a question the raw profile cannot. They are computed over the very bins the profile draws.
The sweep layers detect reversals AT those levels — an order-flow taxonomy (absorption / exhaustion / divergence / rejection) plus a structural stop-run-and-reclaim. A Hawkes self-exciting intensity flags when sweeps are clustering (cascade risk).
A correlation-aware fusion (Kish design-effect shrinkage) combines the firing sweep's realized edge with the concurrent absorption and toxicity tells into a single reversal probability — shrinking redundant, correlated evidence so agreement among related signals cannot masquerade as independent confirmation.
The calibration spine forward-resolves every sweep and reports its hit rate versus base rate with a Wilson confidence interval. This is what ties the stack together: a factor only earns trust if the resolved outcomes say it does.
Remove any one layer and the others lose context: the profile without classification is just a volume histogram; the microstructure factors without the profile have no levels to attach to; the sweeps without calibration are unverified claims. Together they are a single, self-checking read of the auction.
How it works (mechanics)
Intrabar data. Lower-timeframe OHLCV is pulled with request.security_lower_tf (no lookahead). The lower timeframe is auto-derived from the chart timeframe or set manually.
Trade side. Bulk Volume Classification assigns each intrabar a buy fraction from a Student-t CDF of its standardized price change; delta = buy − sell. Where a Premium/Ultimate plan allows it, native request.footprint() real bid/ask volume per price replaces the reconstruction and feeds the identical bins.
Profile build. On the last bar, the chosen lookback of confirmed bars is accumulated into price bins; POC and the 70% Value Area are derived, voids and quadrant deltas computed.
VPIN. Volume is partitioned into equal-volume buckets; the average order imbalance across the last N buckets is the 0–1 toxicity read (with a percentile and background glow).
Impact factors. Stationarized (log-compressed, z-scored) OFI; depth-weighted multi-level OFI across the bins; Kyle's λ as the regression slope of return on signed flow; Amihud illiquidity as |return| per traded value.
Absorption. A displacement-normalized form and a √-law form (realized impact vs Y·ATR·√(|Δ|/V)): below the prediction = passive absorption / reversal candidate; above = fragile expansion.
Sweeps & fusion. At a swept extreme the bar is classified ABS / EXH / DIV / REJ; a Hawkes intensity tracks clustering; a Kish-decorrelated log-odds fusion outputs one reversal probability. Separately, a reclaim-confirmed liquidity sweep fires when price runs a confirmed swing pivot, closes back inside recovering a minimum fraction of the run, on a volume spike.
Calibration. Each sweep is queued and resolved a fixed horizon later against a moveATR·ATR threshold, recorded in R-multiples (MFE / MAE). The dashboard shows, per class: sample count, Hit% ± Wilson interval, Base% (the unconditional reversal rate over the same horizon), Edge (Hit − Base, starred at 95% significance), and average MFE / MAE.
Non-repaint: all detection is on confirmed bars, lower-timeframe arrays are confirmed intrabars, no dynamic-length built-ins are used, and the profile is drawn on the last bar from confirmed history. Pivots used by the liquidity sweep are confirmed before they can be swept.
What makes it original
It is built around calibration, not assertion. Most order-flow tools print a delta, a "confidence," or a footprint and leave it there. Here every reversal signal is forward-resolved against realized price and reported with a base rate and a Wilson interval, so the dashboard distinguishes a real edge from a small-sample illusion.
The agreement between Bulk Volume Classification and a tick rule is surfaced openly — a known weakness of reconstructed order flow is shown rather than hidden.
The microstructure factors are computed over the profile's own bins and decorrelated before fusion, so correlated flow signals don't inflate confidence.
It degrades gracefully from native exchange footprint (Premium) to reconstruction (every plan) with no change to the visual or the workflow.
All factor implementations are original Pine re-derivations of published methods; no code from other scripts is used.
How to use it
Apply to a liquid symbol on an intraday timeframe. Read the profile to see where volume concentrated (POC, Value Area, voids).
Watch the sweep tags and ⚡ liquidity-sweep markers at the edges of the range — these are reversal hypotheses, not guarantees.
Before trusting a sweep class, check its row in the calibration panel (switch the dashboard to Pro): is its Hit% above Base%, is the edge starred (significant), and is the Wilson interval tight enough to mean something?
Use the VPIN glow and fused reversal probability as context: bright background = one-sided / informed flow, which leans toward continuation and makes fades riskier.
The Compact dashboard summarizes the key reads (POC, VPIN, best calibrated edge, fused probability, liquidity-sweep status, auction efficiency); Pro expands the full per-class calibration table and every microstructure row.
Hidden EXP_* data-window series are provided for chaining into other scripts via input.source().
Data & markets
Works on whatever symbol the chart shows — nothing is hard-coded to an exchange or instrument. Defaults suit index futures on an intraday chart. For other instruments, adjust the Profile & Data Source group (lower-timeframe division, profile lookback) and, on a supporting plan, the native footprint settings. Reconstructed order flow is most reliable on liquid instruments with continuous volume.
Concept credits
This script operationalizes published methods; all implementations are original re-derivations.
Tick rule / trade sign — Lee & Ready (1991)
Bulk Volume Classification & flow toxicity (VPIN) — Easley, López de Prado & O'Hara (2012)
Order-Flow Imbalance — Cont, Kukanov & Stoikov (2014)
Multi-level / integrated OFI — Xu, Gould & Howison (2018)
Price impact (λ) — Kyle (1985)
Illiquidity ratio — Amihud (2002)
Self-exciting intensity — Hawkes (1971); Bacry, Muzy et al.
Square-root impact law — Almgren et al.; Tóth, Bouchaud et al.
Effective-sample decorrelation — Kish design effect
Market / auction profile (POC, Value Area) — Steidlmayer
Confidence interval — Wilson score interval (1927)
Disclaimer
For educational and informational purposes only. This is an analytical tool, not financial advice and not a solicitation to trade, and it is not a guarantee of future results. Order-flow classification from OHLCV is an estimate, not the true tape — without a Level-2 order book every delta here is a proxy (native footprint excepted). Always do your own research and manage risk; paper-trade before committing real capital. مؤشر
Sin RSI Footprint (with Volume, CVD & VWAP)[ALT_analyst]
█ Overview
The Sin RSI Footprint brings the concept of professional order flow and footprint charting directly into momentum oscillators.
Instead of mapping traditional trading volume at raw price levels, this script peers inside the current higher-timeframe candle to map the internal momentum distribution and structural shifts using Lower Timeframe (LTF) RSI Crossovers, Volume, and Cumulative Volume Delta (CVD) data.
By visualizing exactly where and how momentum transitions occurred within a single bar, traders can identify hidden exhaustion, hidden accumulation/distribution, and intra-bar divergences that are completely invisible on standard candlestick charts.
█ Why is this one unique
This is not a normal RSI indicator. It is an intra-bar market structure mapping engine.
Most indicators treat every price level within a single candlestick as equal. This script uses a Fast/Slow RSI Cross Trigger mechanism to filter out ambient market noise, isolating the exact coordinates where momentum definitively shifts. It transforms these structural shifts into a multi-dimensional footprint matrix, combining them with localized transaction metrics.
⚪ What it does
At a high level:
• Retrieves LTF Crossover Data: Uses request.security_lower_tf to fetch an array of LTF Fast/Slow RSI crosses, Close prices, Volume, and CVD for the duration of the current chart's bar.
• Bins the data: Groups the validated LTF momentum nodes into precise price levels or vertical stacks.
• Calculates localized momentum: Identifies exactly where the definitive momentum shifts occurred inside the wick or body.
• Applies VWAP Trend Discipline: Dynamically filters out counter-trend noise based on a multi-band session VWAP.
█ Core Engines & How It Works
⚪ 1. The Matrix Engine (Traditional Price Level)
This mode acts like a traditional volume profile or footprint chart, but optimized for RSI crossover nodes and localized liquidity.
The script divides the high-to-low range of the current candle into user-defined bins (e.g., 10 rows). It calculates the step size dynamically:
float _st = _bh > _bl ? (_bh - _bl) / _i21 : syminfo.mintick
For each LTF crossover point, it determines the correct row and calculates the localized average RSI, accumulated Volume, and CVD.
Why this matters: It maps definitive momentum shifts to specific price levels. You can instantly see if buyers were exhausting their momentum at the absolute highs or absorbing pressure in the middle of the candle.
⚪ 2. The Stack Engine (Vertical Momentum)
This mode stacks validated LTF RSI crossover prints vertically above or below the candle based on a baseline threshold.
If the triggered LTF RSI is > 50, it is categorized as bullish and stacked above the candle's high. If <= 50, it is stacked below the candle's low. The height of each box is calculated dynamically using the Average True Range (ATR):
float _bhg = _ba * _i22
Why this matters: Separating crossover prints above and below the candle isolates bullish vs. bearish momentum bursts. Using ATR for box height ensures the boxes remain visually proportionate across different assets and timeframes regardless of absolute price volatility.
⚪ 3. The Adaptive VWAP Filter Engine & Multi-Bands
To enforce strict trading discipline ("No position is the third position"), the script includes an adaptive VWAP transparency filter and a dynamic multi-band system.
If a localized footprint box generates a "Sell" signal (overbought RSI) but the price level is ABOVE the session VWAP (bullish regime), the script automatically increases the transparency of that box, fading it into the background. It does the same for "Buy" signals below VWAP.
This ensures you are only presented with high-probability exhaustion or continuation signals that align with the true intraday value.
█ Key Features
• Micro Momentum Analysis: Displays localized RSI, Volume (K), and CVD directly inside the footprint boxes only when a momentum shift is confirmed.
• VWAP Trend Gate & Gradient Bands: Counter-trend signals are visually faded to prevent fighting the primary trend, visualized alongside 3 dynamic standard deviation bands.
• Noise Filter (Hide Range): Clean up the chart by hiding neutral RSI values (e.g., hiding everything between 30 and 70).
• Responsive Text Scaling: Box text size automatically adapts based on box height and user-defined scaling adjustments.
• Custom Color Themes: Choose from heavily optimized palettes including Neon Cyber, Monochrome Dark, Oceanic Deep, Toxic Alert, and Classic TV.
█ Settings
• Mode: Select the rendering engine (Stack or Matrix).
• Fast/Slow RSI Length (Cross Trigger): Defines the sensitivity of the LTF momentum shift detection.
• Matrix Rows: Specifies how many segments the candle is divided into.
• Lower Timeframe (LTF): Specifies the lower timeframe for data collection. Shorter LTFs provide higher precision but increase historical loading requirements.
• Counter-Trend Signal Transparency Filter: Toggles the adaptive VWAP filter.
• Hide Range Min / Max: Filters out neutral momentum values to isolate true extreme nodes.
█ How to Use
⚪ Spotting Reversals (Matrix Mode)
Look for deep red (overbought) RSI footprints concentrated at the very top of a bullish candle's wick. If the next candle fails to break that high, combined with a negative CVD print in the box, it highly suggests momentum exhaustion and trapped buyers at resistance.
⚪ Confirming Breakouts (Stack Mode)
When price breaks a key level, look at the Stack Mode. A large stack of green/blue boxes above the candle, supported by high relative volume, confirms sustained LTF bullish momentum shifting in your favor.
⚪ Filtering Noise
Set the "Hide Range" to 30-70 and enable the "VWAP Filter". The indicator will now only display footprint boxes when the LTF momentum shift reaches true extremes in the direction of the daily trend.
█ Developer's Note & Timeframe Guidelines
As a fundamental characteristic of market structure, momentum patterns observed on higher timeframes tend to carry greater reliability due to the natural reduction of market noise. To achieve optimal performance and prevent technical limitations (TradingView limits), follow these recommended timeframe ratios:
• For Stack Mode (Recommended Ratio: 1:5 to 1:12)
Best paired as 1m LTF on 5m/10m chart, or 5m LTF on 30m/1H chart.
• For Matrix Mode (Recommended Ratio: 1:12 to 1:60)
Best paired as 1m LTF on 15m/1H chart.
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.
مؤشر
Footprint X-Ray [BOSWaves]Footprint X-Ray - Intrabar Delta Decomposition with Stacked Imbalance, Absorption, and Unfinished Business Detection
Overview
Footprint X-Ray is an intrabar order flow decomposition system that reconstructs the buy and sell volume distribution within each bar by pulling lower timeframe data and mapping participation to price rows, where row coloring, POC identification, stacked imbalance detection, and signal generation are driven by actual delta ratios at each price level rather than bar-level approximations or close-position estimates.
Instead of treating each bar as a single undifferentiated unit of buying or selling pressure, the indicator divides each bar's price range into rows sized relative to ATR, assigns lower timeframe bar volume to each row based on price overlap, and derives a per-row delta ratio that reflects whether buying or selling dominated at each specific price level within the bar. This creates a full participation map inside every candle showing not just what direction the bar moved but where within the bar each side was in control.
This creates an order flow framework that reveals the internal structure of price action invisible on a standard candlestick chart. The footprint cells expose per-level delta composition, the POC identifies the price row with the greatest participation, stacked imbalances highlight consecutive rows with dominant one-sided flow indicating aggressive institutional activity, absorption signals detect when extreme rows show opposing flow against the bar direction, and unfinished business zones project forward from bars where one side was entirely absent at the extreme, marking locations where price is statistically likely to return to complete the auction.
Price is therefore evaluated not at the bar level but at the price row level, exposing order flow dynamics that standard indicators cannot access.
Conceptual Framework
Footprint X-Ray is founded on the principle that the most actionable order flow information lives inside individual bars rather than across them, and that understanding which specific price levels within a bar attracted aggressive buying or selling reveals institutional positioning fingerprints that bar-level indicators systematically obscure.
Standard order flow approaches measure directional commitment at the bar level through delta, volume, or close positioning, but these metrics collapse the internal price distribution into a single reading that loses the structural detail of where within the bar each side dominated. This framework recovers that internal structure by reconstructing per-row participation from lower timeframe data, exposing the distribution of buying and selling pressure across the full price range of every bar.
Three core principles guide the design:
Each price row within a bar should have its own buy and sell volume measurement derived from lower timeframe participation overlap, providing per-level delta ratios rather than bar-level approximations.
Structural patterns within the footprint, specifically stacked consecutive dominant rows and opposing flow at extremes, carry meaningful institutional activity signals that justify dedicated detection and visualization separate from raw row coloring.
Unfinished auction levels where one side was entirely absent at a bar extreme should be projected forward as active reference zones until price returns to complete the participation, as incomplete auctions represent the highest-probability reversion targets within the footprint framework.
This shifts order flow analysis from bar-level delta measurement into per-row intrabar participation mapping where structural footprint patterns expose institutional activity with precision unavailable at the candlestick level.
Theoretical Foundation
The indicator combines lower timeframe OHLCV data retrieval, price overlap-weighted volume allocation to ATR-derived price rows, per-row delta ratio calculation, POC identification by maximum row volume, consecutive dominance run detection for stacked imbalances, extreme row opposing flow detection for absorption, and one-sided extreme row detection for unfinished business zone projection.
Lower timeframe bars are retrieved using security_lower_tf and each lower timeframe bar's volume is allocated to price rows proportionally based on the overlap between the lower timeframe bar's range and each row's boundaries. Bullish lower timeframe bars contribute their allocated volume to buy volume and bearish bars to sell volume, with doji bars split equally. CVD from TradingView's volume delta library provides the bar-level delta for divergence and exhaustion detection. Row size is automatically derived as a fraction of the 200-bar ATR, scaling the footprint granularity to the instrument's typical volatility.
Four internal systems operate in tandem:
Row Construction and Delta Allocation Engine : Divides each bar's price range into ATR-scaled rows, iterates through all lower timeframe bars within the current chart bar, allocates volume to overlapping rows by price range fraction, and derives per-row buy volume, sell volume, total volume, and delta ratio.
Footprint Analysis System : Identifies the POC as the row with maximum total volume, runs consecutive dominance detection in both bull and bear directions to classify stacked imbalance rows, and evaluates extreme rows for absorption by testing opposing side dominance against the configured threshold.
Unfinished Business Zone Engine : Tests the top and bottom rows of each bar for single-sided extreme dominance, creates forward-projecting zone boxes from qualifying rows, extends those zones rightward on each subsequent bar, and removes them when price midpoint is revisited.
Signal Detection System : Derives bar delta from the CVD series, tests for delta divergence against recent price highs and lows, and identifies exhaustion bars where volume significantly exceeds the SMA baseline but net delta remains near zero, indicating a contested bar where neither side achieved directional resolution.
This design provides a complete intrabar participation map with structural pattern detection across every dimension of order flow that is reconstructable from OHLCV data.
How It Works
Footprint X-Ray evaluates price through a sequence of intrabar decomposition and pattern detection processes:
Lower Timeframe Selection : The indicator automatically selects the most appropriate lower timeframe based on the current chart timeframe, using one-second for seconds charts, one-minute for intraday, five-minute for daily, and sixty-minute for higher timeframes, or the manually configured timeframe when auto selection is disabled.
Row Size Calculation : The ATR over 200 bars multiplied by 0.1 and rounded to the minimum tick produces the row height, scaled automatically to the instrument's volatility. Manual row sizing overrides this when auto sizing is disabled.
Lower Timeframe Data Retrieval : OHLCV arrays for the lower timeframe are retrieved via security_lower_tf and CVD is calculated using TradingView's volume delta library, providing both intrabar participation data and bar-level delta for signal detection.
Row Initialization : The bar's price range is divided into rows of equal height, with the number of rows derived from the range divided by the row size.
Volume Allocation per Row : For each row, all lower timeframe bars are iterated. Each lower timeframe bar contributing to a row has its volume allocated proportionally based on the fraction of its range overlapping the row boundary. Bullish lower timeframe bars contribute to buy volume and bearish bars to sell volume.
Delta Ratio Calculation : Each row's delta ratio is calculated as buy volume minus sell volume divided by total volume, producing a normalized score from negative one to positive one that drives cell coloring.
POC Identification : The row with the maximum total volume is identified as the Point of Control, receiving a distinct highlight color and an optional midpoint line.
Stacked Imbalance Detection : Consecutive bullish dominant rows exceeding the configured threshold ratio are identified as bullish stacked imbalances. The same logic in reverse identifies bearish stacks. Qualifying rows receive highlighted border coloring.
Absorption Detection : The top rows of green bars are tested for sell dominance and the bottom rows of red bars are tested for buy dominance. When opposing flow exceeds the absorption threshold at an extreme, an absorption signal is generated at the bar's high or low.
Unfinished Business Zone Creation : The top row of each bar is tested for buy dominance above the UB threshold and the bottom row for sell dominance above the threshold. Qualifying extreme rows generate forward-projecting zone boxes that extend rightward until price revisits the zone midpoint.
Delta Divergence Detection : Price making a new high within the lookback window while bar delta is negative generates a bearish divergence signal. Price making a new low while bar delta is positive generates a bullish divergence signal.
Exhaustion Bar Detection : Bars with volume exceeding the SMA baseline multiplied by the volume multiplier and with absolute delta-to-volume ratio below the configured maximum qualify as exhaustion bars, indicating high participation without directional resolution.
Candle Overlay Rendering : An optional transparent candle overlay with wicks colored by bar delta direction provides directional context on top of the footprint cell display without obscuring the underlying participation data.
Together, these elements form a continuously updating intrabar participation map where cell coloring reveals per-level delta composition, structural pattern detection identifies institutional activity signatures, and forward-projecting zones maintain active auction completion references.
Interpretation
Footprint X-Ray should be interpreted as an intrabar order flow decomposition system with layered structural pattern detection:
Footprint Cells : Each colored box represents a price row within the bar. Green shading indicates buy-dominant flow at that level, red shading indicates sell-dominant flow, and color intensity reflects the magnitude of the imbalance. Neutral rows with balanced participation appear at intermediate opacity.
Cell Text (Delta %) : The percentage displayed in each cell represents the net delta ratio for that row, quantifying how one-sided participation was at each specific price level within the bar.
Cell Text (Volume) : When volume mode is selected, each cell displays the net volume (buy minus sell) at that price row, providing absolute rather than relative participation data.
Accent Lines : A bright horizontal line on the dominant edge of each row highlights the side with greater participation, providing a clean visual boundary that reinforces the directional reading of each cell without requiring the text to be read.
POC Row : The row with the highest total volume receives a distinct highlight and optional midpoint line, marking the price level with the greatest participation concentration within the bar and the most significant auction reference point.
Stacked Imbalance Borders : Rows identified as part of a consecutive dominant run receive highlighted border coloring in the imbalance direction, signaling aggressive institutional one-sided flow across multiple consecutive price levels within the bar.
Absorption Diamonds : Diamond markers below bars indicate bullish absorption where buyers dominated the bottom rows of a red bar, suggesting selling pressure was being absorbed by aggressive buyers. Diamonds above bars indicate bearish absorption where sellers dominated the top rows of a green bar.
Unfinished Business Zones : Forward-projecting shaded boxes from extreme rows where one side was entirely absent mark incomplete auctions. Bullish UB zones project from bars where buyers dominated the top row without seller response. Bearish UB zones project from bars where sellers dominated the bottom row without buyer response.
Delta Divergence Crosses : Cross markers below price on bullish divergences and above price on bearish divergences identify structural disagreement between price direction and bar delta, flagging hidden weakness at new highs and hidden strength at new lows.
Exhaustion Circles : Orange circles on bars with extreme volume but near-zero net delta mark contested bars where neither side achieved resolution despite heavy participation, indicating potential inflection points where the prior directional move may be losing conviction.
Delta Wicks : Optional candle overlay wicks colored by bar delta direction provide an immediate visual cue for whether net buying or net selling dominated the bar as a whole, complementing the per-row cell analysis.
Per-row delta composition, POC location, stacked imbalance presence, absorption signals, unfinished business zones, and exhaustion bars collectively provide more order flow intelligence than any element in isolation.
Signal Logic & Visual Cues
Footprint X-Ray presents five distinct signal types derived from intrabar participation analysis:
Absorption Signal : Diamond markers generated when extreme rows show opposing flow against the bar direction, identifying institutional absorption of aggressive flow at price extremes.
Delta Divergence Signal : Cross markers generated when price makes new highs or lows within the lookback window but bar delta contradicts the directional move, flagging structurally weak breakouts.
Exhaustion Bar Signal : Orange circles generated on bars with exceptional volume and near-zero net delta, identifying participation battles where neither side achieved dominance despite heavy activity.
Unfinished business zones provide continuous passive signal context by projecting forward from incomplete auction extremes until price returns to complete the participation sequence.
Alert generation covers bullish and bearish CVD flips, bullish and bearish absorption events, bullish and bearish delta divergence, and exhaustion bar detection for comprehensive systematic order flow monitoring.
Strategy Integration
Footprint X-Ray fits within institutional order flow and auction theory-based trading approaches:
POC Reaction Trading : Use POC rows as high-probability reference levels within each bar. The highest-volume row represents the price level most accepted by both buyers and sellers and frequently acts as intrabar support, resistance, or reversion anchor on subsequent price interaction.
Stacked Imbalance Directional Bias : Use stacked imbalance detection as a directional conviction signal within bars. Consecutive buy-dominant rows from low to high indicate sustained aggressive buying across multiple price levels, suggesting institutional accumulation rather than isolated speculative activity.
Absorption Reversal Framing : Use absorption signals as potential reversal triggers where aggressive flow is meeting organized opposing participation at extremes. Bullish absorption at the low of a red bar suggests buyers are defending price levels despite selling pressure, while bearish absorption at the high of a green bar suggests sellers are resisting upside extension.
Unfinished Business Zone Targets : Use UB zones as reversion targets for subsequent price action, monitoring whether price returns to complete the auction at levels where one side was previously absent. The completion of an unfinished auction typically involves a return to the zone followed by the missing side finally participating.
Divergence-Confirmed Entries : Use delta divergence signals as structural warning indicators rather than standalone entries, weighting them more heavily when they coincide with other confluence factors such as absorption or unfinished business zone proximity.
Exhaustion Bar Context : Treat exhaustion bars as inflection point alerts requiring subsequent bar confirmation rather than immediate entry triggers. High volume with near-zero delta indicates a contested equilibrium that will resolve directionally on the following bars.
Technical Implementation Details
Delta Source : TradingView volume delta library providing CVD series with automatic lower timeframe selection
Row Construction : ATR-fraction row sizing with lower timeframe price overlap-weighted volume allocation per row
Analysis Engine : Maximum volume POC identification, consecutive dominance run detection for stacked imbalances, and extreme row opposing flow testing for absorption
UB System : Array-managed forward-projecting zone boxes with midpoint revisit detection and automatic removal
Signal Detection : CVD-based delta divergence against lookback window highs and lows, volume SMA ratio combined with delta ratio for exhaustion classification
Visualization : Gradient-colored footprint cells with accent lines, POC highlighting, stacked imbalance borders, absorption and divergence markers, exhaustion circles, UB zones, and optional delta wick candle overlay
Performance Profile : Optimized with object count caps and array management for real-time execution across intraday and higher timeframes
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Intrabar microstructure analysis for scalping with automatic one-minute lower timeframe providing high-resolution participation data within each chart bar
15 - 60 min : Session-level order flow decomposition for intraday trading with sufficient lower timeframe bar count per chart bar to produce meaningful row distributions
4H - Daily : Swing-level institutional footprint analysis with five-minute lower timeframe providing detailed participation mapping across larger price ranges
Suggested Baseline Configuration:
Auto Lower Timeframe : Enabled
Auto Row Size : Enabled
Cell Text : Delta %
Accent Lines : Enabled
Highlight POC Row : Enabled
Show Stacked Imbalances : Enabled
Min Consecutive Rows : 3
Dominance Threshold (SI) : 0.60
Show Absorption : Enabled
Show Unfinished Business : Enabled
Delta Divergence : Enabled
Exhaustion Bars : Enabled
Show Candle Overlay : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the instrument's volume characteristics, typical bar range, and preferred signal sensitivity, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Too few rows per bar : Disable Auto Row Size and manually reduce the row size value to increase row count, producing a more granular footprint with finer price level resolution for the target instrument and timeframe combination.
Too many rows cluttering the display : Increase the manual row size or allow auto sizing to recalibrate. On higher timeframes with large bar ranges the automatic ATR fraction may produce excessive row counts that reduce readability.
Stacked imbalance signals too frequent : Increase the Min Consecutive Rows setting to require longer dominance runs before stacking is classified, or increase the Dominance Threshold toward 0.75 to demand stronger per-row directional conviction.
Absorption signals too frequent : Increase the Absorption Threshold toward 0.80 to require stronger opposing dominance at extremes before an absorption signal fires, filtering for only the most decisive institutional responses.
Too many unfinished business zones : Increase the UB Dominance Threshold toward 0.90 to restrict zone creation to only the most extreme single-sided bar extremes, reducing zone density on the chart.
Divergence signals firing too often : Increase the Divergence Lookback to require price to make a more significant new high or low before the divergence condition tests, reducing signal frequency to only the most structurally significant disagreements.
Exhaustion signals too frequent : Increase the Volume Multiplier to require a larger volume spike above the baseline before exhaustion classification, or decrease the Max Delta Ratio to require closer to zero net delta for a bar to qualify.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Liquid instruments with consistent volume where lower timeframe bar allocation produces meaningful per-row participation distributions and reliable delta measurements
Intraday and session-level timeframes where sufficient lower timeframe bars exist within each chart bar to produce statistically representative row-level volume allocation
Order flow-based trading approaches where intrabar participation patterns provide entry confirmation or invalidation context that bar-level indicators cannot supply
Institutional activity monitoring where stacked imbalances and absorption patterns reveal aggressive positioning that precedes significant directional moves
Reduced Effectiveness:
Low-liquidity instruments where thin lower timeframe volume produces sparse row distributions with many empty cells and unreliable per-level delta ratios
Higher timeframes on instruments without lower timeframe data availability where security_lower_tf returns insufficient bars per chart bar for meaningful decomposition
Instruments without volume data, which the indicator detects and blocks with a runtime error
Extremely fast markets during news events where lower timeframe bars are so large relative to the chart bar range that overlap allocation produces distorted row distributions
Thin or extended-hours sessions where volume is too low to produce statistically meaningful per-row participation differences distinguishable from random distribution
Integration Guidelines
Confluence : Combine with BOSWaves structural tools, trend indicators, or momentum oscillators to validate footprint-derived signals with broader analytical context before acting on intrabar participation patterns
POC Context : Treat POC rows as the most significant intrabar reference levels. Subsequent price returning to a prior bar's POC is returning to its highest-participation level, which frequently acts as support, resistance, or magnetic reversion anchor.
Stacked Imbalance Direction : Use stacked imbalance direction as a short-term institutional bias indicator. Consecutive buy-dominant rows from low to high suggest aggressive accumulation that may continue on subsequent bars. Consecutive sell-dominant rows suggest distribution.
Unfinished Business Patience : Allow UB zones to be approached naturally rather than anticipating reactions immediately after formation. The auction completion process can take multiple bars and the zone should be monitored for participation behavior on arrival rather than treated as an automatic reversal level.
Exhaustion Confirmation Requirement : Never treat exhaustion bars as standalone entry triggers. The exhaustion condition identifies a contested state that requires subsequent directional resolution. Wait for the following bar to confirm which side won the participation battle before acting on the exhaustion signal.
Disclaimer
Footprint X-Ray is a professional-grade intrabar order flow decomposition and institutional activity detection tool. It uses lower timeframe volume allocation with per-row delta calculation but does not access true exchange-level bid and ask data. All participation measurements are reconstructed from OHLCV data and represent best-approximation estimates rather than actual order book information. Results depend on instrument liquidity, lower timeframe data availability, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, trend context, and comprehensive risk management. مؤشر
Aurora: Order Flow Liquidity HeatshelvesAurora — Order-Flow Liquidity Heat Shelves
Aurora reads the volume footprint inside each bar and marks the price levels where order flow actually concentrated. Not at pivots and not at round numbers like we Traders normally like to do manually. It marks the rows where real volume traded, sorted into two honest categories and tracked across their whole life until they break.
Most "liquidity" indicators draw a box wherever volume was high and call it support. Aurora splits that into two things that behave very differently.
Absorption walls are high volume with balanced delta, printed in the extreme third of the bar. That signature is passive limit orders eating aggression. These are the levels worth calling support and resistance. Demand below, supply above.
Initiative node s are the rows with the largest one-sided delta. That is aggressor commitment, market orders pushing a direction. It often gets defended, but it is not resting liquidity, so Aurora labels it separately and never pretends it is the same thing. If you only want the genuine walls, turn initiative detection off.
Rows are volume-gated first, then candidates are picked. A row has to clear a fraction of the bar's busiest row before it is even considered.
Levels have a lifecycle, not just a birthday
Every bar, each active shelf gets one of three verdicts:
Defended. A wick enters the band and the bar closes back on the correct side. Touch count goes up, the level stays.
Broken. Price closes beyond the far edge by a configurable ATR buffer. The buffer kills noise breaks and is gap proof. Closes inside the buffer stay pending, no verdict.
Flipped. A broken wall retested from the other side and rejected is reborn with inverted polarity. Broken demand that starts acting as resistance becomes supply. Once per shelf.
Overlapping same-side, same-type nodes merge instead of stacking. Volume and delta accumulate, the band widens to the union, and the activity clock resets so a level that keeps getting defended outlives the memory window.
Key walls are chosen by strength divided by distance, not distance alone
A triple-defended level two ATR away can outrank a fresh weak node sitting right next to price. Spotlighted walls on the same side are forced apart by a minimum gap so two adjacent shelves cannot both burn a spotlight on the same zone. Brightness scales with the strength percentile, so the strongest shelf on screen glows hardest and the rest fall off from there.
The cockpit
Shows the nearest spotlighted supply and demand with live ATR distance, window cumulative delta, current bar delta, and the session POC. The distances re-evaluate every tick, so price running through a wall intrabar just promotes the next one rather than printing a negative number. The break and flip verdicts still wait for the bar to close.
Alerts
Four classic alertcondition hooks for new wall, defended, broken, and flipped. There is also an optional JSON webhook mode that emits one structured payload per event, built for piping into your own backend.
IMPORANT, PLEASE READ THIS:
This needs footprint data, which means a TradingView Premium or Ultimate plan. Free and Essential plans cannot access it.
`request.footprint()` reconstructs historical bars from lower timeframe data using tick-rule delta classification. That means historical shelves are approximations. Live shelves, forming in front of you, are the real thing. Treat the history as a decent estimate of where flow clustered, not as tick-exact truth. That means in replay mode Aurora will not show you the truth!
Aurora is a map of where liquidity sat and how it held up. It is not a buy or sell signal and it will not tell you direction. A defended wall can still break on the next bar. Use it for context, for where you expect reactions, and for where your stop is sitting relative to real volume.
Settings
Footprint Engine
Ticks per footprint row. The price height of each volume node, measured in ticks. Smaller values cut the bar into finer price slices and give you more precise levels, at the cost of more rows to chew through. Bump it up on high-priced or volatile symbols where one tick is tiny.
Value Area %. The share of volume counted as the value area when the footprint is built. Standard is 70. Leave it unless you know why you are changing it.
Shelf memory (bars). How many bars of inactivity a shelf can sit through before it gets dropped. This counts inactivity, not age. Every defense or merge resets the clock, so a level that keeps getting respected can live far longer than this number suggests.
Volume gate. A row has to hold at least this fraction of the bar's busiest row to be considered at all. This runs before any candidate is picked, so it is your first filter against noise. Raise it to keep only the heaviest rows, lower it to catch more.
Max shelves. A hard cap on stored shelves. When you hit it, the weakest broken shelves get evicted first, then the weakest overall. Mostly a performance guard.
Node Detection
Detect absorption walls: Turns on the genuine walls, the high volume balanced delta rows in a bar's extreme third. If you only trust passive liquidity, this is the one to keep.
Absorption max delta over volume: The strictness dial for what counts as absorption. A row qualifies only if its absolute delta divided by total volume is at or below this. Lower means stricter, demanding more balanced two-sided flow. Raise it if you are getting too few walls and want to loosen the definition.
Detect initiative nodes: Turns on the max delta rows, the aggressor commitment levels. Useful context, but remember these are not resting liquidity. Turn this off if you want a cleaner chart with walls only.
Break buffer (×ATR): How far past a band's far edge price has to close before the level is called broken, in ATR. This kills noise breaks. Closes inside the buffer stay pending with no verdict. Widen it on choppy symbols to stop premature breaks, tighten it if breaks feel too slow to register.
Allow S/R flips: Lets a broken wall that gets retested from the other side and rejected come back with inverted polarity. One flip per shelf. Turn it off if you would rather a broken level just stay dead.
Visuals
The four color inputs: set absorption demand, absorption supply, initiative buying, and initiative selling. They are kept distinct on purpose so you can read kind and side at a glance.
Key walls per side: How many walls to spotlight above and below price. These are ranked by strength divided by distance, not distance alone.
Min gap between key walls (×ATR): Spotlighted walls on the same side have to sit at least this far apart. Inside a cluster the strongest one wins the slot and the next distinct level gets promoted, so two adjacent shelves cannot both burn a spotlight on the same zone. Set it to 0 to switch this off.
Glow zone height (×ATR): The vertical size of the glow around a key wall. Pure cosmetics, set it to whatever reads well on your chart.
Max core brightness: How bright the strongest shelf's core line gets. Weaker shelves scale down from here by strength percentile, so this is really the top of the brightness range.
Show minor walls: Draws the faint background walls that did not earn a spotlight. Turn it off for a minimal chart with key walls only.
Minor wall thickness (ticks): The drawn height of those faint minor walls. Display only, it does not change detection.
Project forward (bars): How far the walls and labels extend to the right of the current bar. Set to 0 to stop at the live bar.
Hide walls beyond % from price: A render-time filter that hides walls farther than this percent from current price, so the price scale stays near the action. Set to 0 to show everything.
Fade broken walls: Draws broken walls more transparently than active ones. Off draws them at the same weight.
Alerts
Fire JSON webhook alerts: Emits one structured JSON payload per event, new wall, defended, broken, or flipped, on bar close. This is the one for piping into your own backend. The four classic alertcondition hooks fire regardless of this toggle, so leave it off if you just want plain TradingView alerts.
Dashboard
Show order-flow cockpit: Toggles the live panel showing nearest supply and demand, window and bar delta, and POC.
Text size: Sizing for the cockpit text, tiny through large. مؤشر
Order Flow Microstructure Engine# Order Flow Microstructure Engine
**Order Flow Microstructure Engine** condenses a full stack of order-flow and market-microstructure measures into a single decision: one confidence %, one tier, and one action with entry/stop levels — shown in an adaptive on-chart dashboard. It is built to answer one question on every bar: *are aggressive buyers or aggressive sellers in control, and how convinced should you be?*
This is not a bundle of unrelated indicators placed on one chart. Every component measures a **different facet of the same process** — the buy/sell auction happening inside each bar — and they are combined inside **one probabilistic model**. The reason for the mashup, and how the parts interact, is described below as the guidelines require.
**Why these components are combined (mashup justification)**
No single order-flow measure is reliable alone: raw delta misleads during absorption, CVD drifts, footprint imbalances appear in chop, and toxicity rises at both reversals and breakouts. Because these weaknesses are *partially independent*, fusing the measures correctly cancels noise that any one of them carries. The original element is **how** the fusion is done — not what is plotted.
**How it works (the pipeline)**
1. *Reconstruction.* Lower-timeframe sub-bars are pulled and each is classified buy/sell with a tick-rule cascade (after Lee & Ready). Where the data plan exposes native volume footprint, real bid/ask is used and aggregated into the same price bins. The dashboard always shows whether it is running on reconstructed (`RECON`) or native (`NATIVE`) data.
2. *Factors.* From that base it derives Aggressor Imbalance Ratio, Cumulative Volume Delta, footprint imbalances/POC, auction Value-Area efficiency (acceptance vs rejection), integrated multi-level Order-Flow Imbalance (depth-weighted), VPIN-style flow toxicity, Kyle's lambda price-impact/liquidity, a directional self-exciting (Hawkes) intensity, and a square-root-law absorption measure.
3. *Fusion (the original part).* Each factor is mapped to a probability and combined in Bayesian log-odds. Crucially, the flow-derived factors are **decorrelated before fusion** using an effective-sample-size (design-effect) shrinkage, so factors that are really the *same evidence* (AIR, delta, CVD, footprint, OFI) cannot inflate confidence just by agreeing. Structurally independent factors (auction efficiency, MOC, Hawkes, absorption, cross-instrument) enter at full weight. The posterior is then gated by multi-timeframe and multi-horizon consensus and damped by a regime-thrash (chop) penalty, producing one confidence → a 5-tier ladder → an action.
Without this combination you would get several conflicting opinions; the value is the **correlation-aware fusion** that turns them into one calibrated read.
**How to use it**
- Apply to a liquid instrument on an intraday timeframe (1m–1h). Defaults are tuned for index futures on 5-minute charts with 5-second sub-bars.
- Read the Compact dashboard top-down: ACTION + confidence %, Tier (position-size guidance), Entry/Stop, then CO-FIRE confluence, multi-timeframe and regime/stability. Switch "Table view mode" to Full for a complete factor-by-factor breakdown.
- On-chart triangles mark Tier-1/Tier-2 long/short signals across history; footprint boxes show the intrabar buy/sell distribution.
- For other markets: change the **Market preset** (group 01). Choose **CUSTOM** to set your own session, MOC window, footprint bin sizing and CVD reset — making it usable on any instrument in any market.
- The dashboard theme auto-adapts to a light or dark chart background.
**What makes it original**
Correlation-aware decorrelated Bayesian fusion of order-flow factors; a native-footprint seam that uses real bid/ask when available and transparently falls back to reconstruction; layered multi-horizon + multi-timeframe + cross-instrument confirmation on a microstructure base; and a regime-stability filter that penalizes only genuine directional reversals, not same-direction intensity changes.
**Honesty / limitations**
On most retail feeds, order flow here is reconstructed from lower-timeframe data via the tick rule (~75–80% trade-sign accuracy), not true exchange bid/ask, unless your plan provides native footprint data. The data source is shown in the dashboard. This is an analytic and educational tool, not financial advice and not a guarantee of results.
**Concept credits**
Lee & Ready (trade sign); Kyle (price impact / lambda); Easley, López de Prado & O'Hara (VPIN); Cont, Kukanov & Stoikov and Xu, Gould & Howison (OFI / multi-level OFI); Hawkes and Bacry–Muzy (self-exciting intensity); Almgren and Tóth–Bouchaud (square-root impact law); Kaufman (Efficiency-Ratio adaptation); Steidlmayer (Market Profile / Value Area); Kish (design effect). All Pine implementations are original re-derivations; no external script code is used.
**Disclaimer**
For research and educational purposes only. Nothing here is financial advice. Markets carry risk and past behaviour does not guarantee future results. Always do your own research and manage risk.
مؤشر
Sin RSI Footprint■ Overview
The Sin RSI Footprint【ALT_analyst】 indicator brings the concept of footprint charting to momentum oscillators.
Instead of mapping trading volume at price levels, this script peers inside the current higher-timeframe candle to map the internal momentum using Lower Timeframe (LTF) RSI data.
By visualizing exactly where and how momentum was distributed within a single bar, traders can identify hidden exhaustion, hidden accumulation/distribution, and intra-bar divergences that are invisible on standard charts.
■ Core Modes & How It Works
The script utilizes request.security_lower_tf to fetch an array of LTF RSI and Close prices for the duration of the current chart's bar. It then processes this data in one of two distinct visualization modes:
1. Matrix Mode (Traditional Price Level)
This mode acts like a traditional footprint or volume profile, but for RSI.
■ The Calculation
The script divides the high-to-low range of the current candle into user-defined bins (e.g., 10 rows). It calculates the step size:
step = (high - low) / Matrix Rows
For each LTF data point, it determines the correct row using:
math.floor((close - low) / step)
Why this calculation is used
To map momentum to specific price levels, allowing you to see if buyers or sellers were exhibiting strong momentum at the extremes or the middle of the candle.
Actual Output Values
The script outputs an averaged RSI value (ranging from 0.00 to 100.00) for each specific price row. The boxes are colored based on this average (0-9 for extreme oversold, 90-100 for extreme overbought).
2. Stack Mode (Vertical Momentum)
This mode stacks LTF RSI prints vertically above or below the candle based on a baseline threshold.
■ The Calculation
By default, if the LTF RSI is > 50, it is categorized as bullish and stacked above the candle's high. If <= 50, it is stacked below the candle's low. The height of each box is calculated dynamically using:
box_height = ATR * Box Height Multiplier
Why this calculation is used
Separating prints above and below the candle isolates bullish vs. bearish momentum bursts. Using ATR for box height ensures the boxes remain visually proportionate across different assets and timeframes regardless of absolute price volatility.
Actual Output Values
The output generates box coordinates (Top, Bottom, Left, Right) relative to the chart's price scale. The text inside represents the exact LTF RSI value at that sequence point (e.g., 72, 34).
■ Key Features
Noise Filter (Hide Range): Clean up the chart by hiding neutral RSI values (e.g., hiding everything between 40 and 60). This leaves only the significant momentum extremes visible.
Highlight & Enlarge: Automatically expand the width and height of boxes that contain extreme RSI readings (e.g., > 80 or < 20) to instantly draw your eye to critical exhaustion points.
Compression Logic: Consecutive LTF RSI prints that fall into the same color tier and threshold are grouped into a single, taller block to prevent chart clutter.
Custom Color Themes: Choose from Normal, Aurora, Rainbow, or Monochrome to suit your chart background.
Rendering Modes: Includes a Real-time mode for live trading and an "Ultra-Light" Historical Camera Track mode to efficiently review past data without exceeding Pine Script's drawing limits.
■ How to Use
1. Spotting Reversals (Matrix Mode)
Look for deep red (overbought) RSI footprints concentrated at the very top of a bullish candle. If the next candle fails to break that high, it suggests momentum exhaustion at resistance.
2. Confirming Breakouts (Stack Mode)
When price breaks a key level, look at the Stack Mode. A large stack of green/blue boxes above the candle confirms sustained LTF bullish momentum driving the move, rather than a single anomalous tick.
3. Filtering Noise
Set the "Hide Range" to 35-65. The indicator will now only display footprint boxes when the LTF RSI reaches true overbought/oversold extremes, making it highly effective for identifying turning points.
■ Developer's Note
As a fundamental characteristic of the RSI, momentum patterns observed on higher timeframes tend to carry greater reliability due to the natural reduction of market noise. To capture the purest momentum shifts, it is recommended to begin your analysis on larger timeframes.
Disclaimer: This script maps mathematical momentum and does not guarantee future price movements. It is best used in conjunction with price action and broader market context. مؤشر
Delta by Price (Delta Volume Profile)🔹 Introduction
This indicator, Delta by Price (Delta Volume Profile), takes the familiar concept of a volume profile and replaces raw traded volume with net directional volume (delta) at each price level. Instead of showing how much volume traded at a price, it shows which side was more aggressive at that price — buyers or sellers.
The idea is straightforward: if a price level absorbed significantly more aggressive buying than selling (or vice versa), that level likely represents a meaningful shift in who was in control of the auction at that point in the range.
One thing to be upfront about: true tick-by-tick delta isn't available to Pine scripts. This indicator approximates delta by pulling lower-timeframe bars within each higher-timeframe bar and classifying each LTF bar's volume as buy-side or sell-side based on whether it closed above or below its open. It's a proxy, not a recording of the actual order book — but it's the same proxy nearly all publicly available delta tools use, and it tends to track real aggressor flow reasonably well over meaningful sample sizes.
🔹 The Premise / Background Theory
🔸 Volume tells you "how much," delta tells you "who pushed"
A standard volume profile answers one question: how much volume traded at each price? It's useful for finding high-volume nodes (areas of acceptance) and low-volume nodes (areas of rejection), but it's directionally blind. A price level with 1,000 contracts could be 500 aggressive buys and 500 aggressive sells — pure equilibrium — or it could be 950 buys and 50 sells, meaning that level was overwhelmingly bought into.
Delta separates these two scenarios. A delta profile takes that same volume and splits it by aggressor side, then nets it. The result is a histogram that shows not just where volume concentrated, but which direction the pressure leaned at every price.
🔸 A concrete example
Assume price moves through a range from 5,000 to 5,010 over a session.
At the 5,002 level, three separate visits occur. On the first visit, an LTF bar closes higher than it opened with volume of 200 — classified as buy-side. On the second visit, another bar closes lower than it opened with volume of 150 — sell-side. On the third visit, a bar closes higher with volume of 300 — buy-side.
Net delta at 5,002 = +200 − 150 + 300 = +350.
Total volume at 5,002 = 200 + 150 + 300 = 650.
A standard volume profile would just show "650 contracts traded here." The delta profile shows +350 net buying — meaning roughly 54% more buy-side aggression than sell-side at that exact price. If you saw a level like this near the low of a range, it might suggest buyers stepped in there with conviction, not just that "a lot happened" there.
🔸 Why distribute delta across a bar's range instead of just its close
Each higher-timeframe bar has a high and a low, and the LTF bars that compose it trade across that entire range — not just at the close. This indicator takes each HTF bar's net delta and spreads it proportionally across every price bin the bar's high-to-low range touches.
This is an assumption, not a measurement. In reality, delta within a single bar isn't evenly distributed across its range — more of it likely occurred near where price spent the most time. But without LTF-by-LTF price-level tracking (which would be computationally heavy and hit Pine's lower-timeframe data limits quickly), even distribution across the bar's range is the most defensible simplification available. Wider bars contribute a thinner "smear" of delta per price bin; narrow bars concentrate their delta into fewer bins. Over a large enough sample, this tends to average out reasonably well.
🔹 How It Works
🔸 Profile Range: Session vs. Rolling Lookback
The indicator builds its profile from one of two data windows, selectable in settings.
Session mode mirrors how a session volume profile works — it resets at the start of each new session (defined by the session time input) and accumulates only the bars within that session. This is the natural choice if you want to see, for example, today's regular trading hours delta distribution reset cleanly each day, the same way you'd look at a daily session volume profile.
Rolling Lookback mode instead uses a fixed number of the most recent closed bars (configurable, default 200), regardless of session boundaries. This is useful for a continuously updating view of recent delta structure that isn't tied to calendar sessions — helpful for instruments or sessions that don't fit a clean daily reset (e.g. 24-hour futures markets).
There are limitations here worth noting. Session mode depends on the session time input matching how you actually think about your trading day. If you trade through multiple sessions (e.g. Asian, London, NY) and only select one as your "session," the profile will reset and rebuild only around that window — bars outside it are ignored entirely.
🔸 Number of Price Rows
This setting controls how finely the price range is divided into bins — effectively the "resolution" of the profile. A higher row count gives more granular price-level detail but spreads the available delta across more bins, making each individual bin's bar shorter and potentially noisier. A lower row count aggregates more price action into each bin, producing a smoother, more visually digestible profile but losing some precision about exactly where within a price cluster the delta concentrated.
This is a resolution-versus-noise tradeoff — there's no universally correct setting, and it's worth adjusting based on the instrument's typical range and tick size.
🔸 Extend Direction
The profile can be drawn extending to the right of the current bar (the default, useful when you want the profile visible without obscuring recent price action to the left) or to the left, anchored at the start of the lookback/session window — placing it where the data actually begins, similar to how some volume profile tools anchor to the left edge of the range being measured.
This is purely a visual/layout preference and doesn't change any underlying calculation — it only affects where the horizontal delta bars are drawn relative to price.
🔸 Point of Control (POC)
When enabled, a label marks the price bin with the highest total absolute volume (buy-side + sell-side combined, not net delta) — analogous to the POC on a standard volume profile. This identifies where the most total activity occurred, regardless of which direction it leaned. It's possible — and informative — for the POC bin to have a relatively small net delta despite high total volume, which would indicate that level saw heavy two-sided participation rather than one-sided conviction.
🔸 Custom Lower Timeframe
By default, the indicator automatically selects a lower timeframe for delta calculation based on your chart's timeframe (1-second charts use 1S, intraday charts use 1-minute, daily charts use 5-minute, and anything larger uses 60-minute). You can override this manually.
The tradeoff here is precision versus data availability. A finer LTF gives a more granular delta classification per HTF bar, but request.security_lower_tf() has practical limits on how many LTF bars it can return per HTF bar — on very large lookbacks with a very fine LTF relative to your chart timeframe, you may not get the full intrabar picture for older bars.
🔹 Closing Remarks
A delta-by-price profile doesn't tell you why buyers or sellers were more aggressive at a given level — only that they were, based on a reasonable proxy for aggressor classification. Large net-delta clusters don't guarantee future support or resistance. They're best treated as a layer of context: a way of seeing whether the volume that built a price level was directionally lopsided or balanced, which can complement (not replace) your read of structure, location, and broader order flow.
Used alongside the rest of your framework, it's another lens for asking the same underlying question every footprint-based approach asks: was this level built by conviction, or by indecision? مؤشر
مؤشر
Parallel Transport & Holonomy [DAFE]Parallel Transport & Holonomy - (⟡ PTH)
This publication presents the quantitative mechanics and theoretical architecture behind the Parallel Transport & Holonomy engine. This system models price and volume dynamics as trajectories on a continuous two-dimensional manifold. By computing the parallel transport of multi-scale momentum vectors, tracking their covariant derivatives, and integrating their path-dependent rotational deficit ( Holonomy ), the indicator identifies macro regime shifts, structural pivots, and trend continuations.
The architecture is entirely self-contained, featuring a Legit Footprint Toggle that automatically falls back to an advanced Synthetic Footprint Engine when tick-level footprint data is unavailable. It is strictly anchored by a Rolling Confidence Matrix (RCM) and modulated by an Ensemble Dynamic Volatility Scaling (DVS) engine.
The core principle of this system is geometric invariance: signal evaluation is not based on static, lagging thresholds but on the coordinate-invariant deviation of momentum from its natural geodesic path.
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Riemannian Price-Volume Manifold & Metric Tensor
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We define the market state as a coordinate point on a two-dimensional Riemannian manifold
M, where the coordinates are:
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Parallel Transport & Covariant Derivatives
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Let V be a momentum vector on the tangent space of the manifold, constructed at three distinct temporal scales: Fast (vecFast), Medium (vecMed), and Slow (vecSlow). The coordinate components of the vector scale k are computed as:
Geodesic Deviation
A path is geodesic if its covariant derivative along the direction of travel is zero (representing the path of least resistance on the manifold). We define the geodesic deviation Zgeodesic as the rolling z-score of the absolute transport deviation:
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Holonomy & Phase Accumulation
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Holonomy describes the geometric phenomenon where parallel transporting a vector along a closed loop returns a vector with a changed orientation. This angular deficit directly measures the total curvature enclosed by the loop.
Holonomy Group Classifications
Based on the magnitude of the accumulated rotation (HolonomyNorm), the local coordinate space is classified into four geometric groups:
Trivial (Norm<0.10): The manifold is flat; parallel transport is path-independent, indicating quiet consolidated ranges.
Abelian (Norm<0.25): Minor commutative drift; the vector field undergoes predictable linear shifts.
Non-Abelian (Norm<0.50): Significant non-commutative curvature. Parallel transport is highly path-dependent, signaling complex trend development and institutional accumulation.
Singular (Norm≥0.50): Severe rotational deficit. The manifold geometry has collapsed or rotated sharply, marking high-probability reversal peaks and structural transitions.
Holonomy Regime Shifts
A Regime Shift is triggered when HolonomyNorm exceeds the user-defined threshold (i_holoThresh). Depending on the sign of the accumulated loop (sgn(H)) and the divergence between the fast and slow vectors, the engine classifies the transition into one of the following states:
Bull Rotation : Enclosed path yields a positive rotational deficit combined with positive multi-scale momentum.
Bear Rotation : Enclosed path yields a negative rotational deficit combined with negative multi-scale momentum.
Geodesic Break : Extreme localized acceleration causing the momentum field to tear away from its expected trajectory.
Scale Divergence : High-frequency (fast) and low-frequency (slow) vectors rotate in opposite directions, enclosing a singular point.
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Rolling Confidence Matrix (RCM)
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The Rolling Confidence Matrix (RCM) acts as a structural anchor. It takes multi-scale geometric indicators from the transport engine and compiles them into a state-space model that dynamically controls signal permissions.
State Space Representation
The matrix maintains a rolling assessment of market structure across three distinct states:
Established (State 0): The trend is structurally intact; manifold bands are compressed, and parallel transport confirms high directional persistence.
Transition (State 1): Price is approaching key structural levels or experiencing a holonomy spike. Manifold bands widen, and standard signals are gated.
Damaged (State 2): Price has broken through primary defensive corridors. Structural confidence is compromised; bands expand by the Damage Band Expansion multiplier
(i_damageWiden), and counter-trend positions are heavily penalized.
External Evidence Injection
On every bar, the system injects external geometric parameters from the manifold:
This evidence is integrated into the RCM, outputting structural permissions (allowLong, allowShort) and score modulations that are applied to the final signal scoring calculations.
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Ensemble Dynamic Volatility Scaling (DVS)
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To adapt the mathematical models to varying market volatility, the script runs three independent volatility estimators concurrently on every bar:
Ensemble Combinatorics
The baseline values are calculated as moving averages over the memory period. The script then computes the ratios of the current estimators to their historical baselines:
This ratio dynamically scales the width of the holographic manifold bands and the RCM damage thresholds: during highly volatile regimes, thresholds are expanded to prevent whipsaws; during compression regimes, thresholds are tightened to capture early breakouts.
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Footprint & Synthetic Fallback Engine
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The script includes a Use Legit Footprint toggle (i_useFp). When enabled on charts supporting tick databases, the system requests official exchange-traded order flow data:
fp=request.footprint(i_fpTicks,i_fpVA)
The Advanced Synthetic Footprint Engine
If the user deactivates the footprint toggle, or if the asset/timeframe does not support tick database access (e.g., historical bars or Forex), the Synthetic Footprint Fallback Engine** takes over, simulating volume-at-price and directional delta using high-frequency mathematical approximations:
This estimated delta reconstructs the synthetic buy and sell pressures:
Kyle's Lambda (Price Impact & Liquidity Risk)
Using these directional volumes, we calculate Kyle's price impact coefficient λ, measuring the price change per unit of volume flow:
High values of λ represent illiquid, thin, or toxic conditions. The system calculates the z-score of lambda (Zλ). If Zλ > 2.0, signal scores are dynamically penalized to protect capital from low-liquidity slippage.
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Structural Scoring & Signal Gating
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The system generates a directional Signal Score using a two-tier quantitative scoring process.
Stage 1: Structural Base (Minimum of one required)
Siege Corridor Proximity : Price is within 1.2 ⋅ ATR of a verified support or resistance level.
ScoreBase = 0.28+(Hold Probability⋅0.18)
EMA21 Geodesic Pullback : Price pulls back to the EMA21 in trend alignment.
ScoreBase = 0.25
RSI Extreme Exhaustion : RSI is below 25 (for longs) or above 75 (for shorts).
ScoreBase = 0.28
Stage 2: Confirmations (Additive)
Multi-pattern confluence (2+ patterns matching): +0.15
Pressure bar strength exceeds thresholds: +0.08 to +0.15
Metric curvature aligns with structural turn: +0.10
Volume expansion exceeds historical average: +0.10
Holonomy phase shifts or loop spikes: +0.12
Directional Scaling & Gating
The score is adjusted based on trend alignment. With-Trend (WT) setups (longs above the EMA50, shorts below) are evaluated using standard thresholds. Counter-Trend (CT) setups face a strict scaling penalty:
ScoreCT = ScoreRaw ⋅ clamp(1.0−Trend Strength⋅0.15,0.45,1.0)
Additionally, the score passes through three gates:
Liquidity Penalty : Reduced by up to 30% if order flow thickness is classified as Desert or Frozen.
Kyle Lambda Gate : Penalized if price impact is highly volatile (Zλ > 2.0).
Danger Gate : Score is multiplied by 0.60 if the composite risk danger index exceeds 75.
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Input Reference
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═══ GEOMETRY ENGINE ═══
Hurst Period (30 to 200, default 80): Lookback window for R/S calculations to evaluate trend persistence.
Pivot Length (2 to 15, default 5): Lookback/lookahead buffer for swing pivot detection.
TF Preset (Auto, 1m-5m Scalp, 10m-15m Intra, 30m-1H Swing, 4H-D Position): Set the timeframe scaling preset. Auto dynamically scales lookbacks based on the current chart timeframe.
Use Legit Footprint (Boolean, default true): Toggles whether to query exchange tick database for real footprint data.
Footprint Ticks/Row (1 to 500, default 100): Height of the footprint rows in tick units.
Footprint VA % (50 to 90, default 70): Percentage of volume enclosed within the Value Area.
═══ TRANSPORT & HOLONOMY ═══
Vector Fast Scale (3 to 21, default 8): Periods for tracking short-term momentum vectors.
Vector Medium Scale (10 to 55, default 21): Periods for intermediate momentum geometry.
Vector Slow Scale (21 to 144, default 55): Baseline vector window representing the macro manifold.
Holonomy Window (13 to 89, default 34): Size of the temporal loop used to integrate local deviations.
Holonomy Spike Threshold (0.10 to 0.90, default 0.35): Sensitivity threshold for identifying rotational shifts.
Geodesic Deviation Sensitivity (0.5 to 3.0, default 1.5): Sensitivity for flagging off-path momentum.
Enable Continuation Signals (Boolean, default true): Toggles secondary pullback entries in strong trends.
═══ ROLLING CONFIDENCE MATRIX ═══
Block Transition Signals (Boolean, default true): If active, blocks standard entry signals when the matrix is in transition state.
Conf Band Tightening Factor (0.0 to 1.0, default 0.35): Compression multiplier for manifold bands under high confidence.
Damage Band Expansion (1.0 to 1.5, default 1.15): Expansion multiplier for manifold bands under structural damage.
═══ DYNAMIC VOLATILITY SCALING (DVS) ═══
Enable Ensemble DVS Engine (Boolean, default true): Toggles whether to scale target multiples and thresholds by volatility.
DVS Method (ATR Ratio, Parkinson, Garman-Klass, Ensemble): Method for calculating real-time volatility.
DVS Memory Period (20 to 200, default 100): Baseline lookback window for historical volatility.
DVS Sensitivity (0.3 to 1.5, default 0.7): Exponent scaling parameter for the DVS ratio.
═══ SIGNAL GATING ═══
Signal Threshold (0.10 to 0.85, default 0.40): Minimum score required to fire a signal.
Cooldown Bars (1 to 50, default 6): Minimum bar spacing between consecutive signals.
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Visual System & Dashboard Interpretation
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Transport Field Manifold : A multi-layered band surrounding price. It contracts during clean, high-confidence geodesic flow and expands during high-entropy, damaged, or volatile regimes. Its color is determined by the RCM state: Green for bullish stability, Red for bearish, and Purple during transitions.
Siege Corridors : Plotted as horizontal support (Green) and resistance (Red) zones with an ATR-scaled boundary. At the right-hand side of the chart, these corridors display critical metrics: the total historical hit count, the estimated break probability (Pbreak), and the fail/reversal probability (Pfail).
Vector Field Lines : Small arrow-headed lines plotted on every third bar. They show the mathematical direction of the Fast momentum vector. They visually represent the difference between the actual observed path of the market and the expected parallel-transported baseline.
Holonomy Phase Ring : An indicator plotted at the right edge of the chart displaying the current phase angle (e.g., ◐ 144∘).
Glitch Markers: Flagged on the chart as ◈ GLITCH when anomalous pricing data or sudden coordinate jumps are detected on the manifold.
Dashboard: Displays real-time metric readings from all quantitative modules:
Tensor Metrics: Curvature, Entropy, Hurst Exponent, and Price Pressure.
Volatility: Dynamic DVS Ratio, active estimator method, and scaling state.
RCM Brain: State classification, integrity index, and gate permissions.
Performance: Total trades, win rate, total profit (R-multiple), profit factor, and max drawdown.
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Limitations and Disclaimers
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This tool is for analytical purposes only and does not predict future market direction.
Historical Simulation: The built-in simulator represents hypothetical performance on historical data. Past results do not guarantee future performance.
Volume Approximations: The synthetic footprint engine uses OHLCV approximations to estimate volume-at-price and directional delta. It is not an actual connection to L2 exchange data feeds or a central clearing book.
Persistence: Reinforcement learning weights are state-space variables that exist within the local execution thread; they reset to 1.0 on every chart load or browser reload.
— Dskyz. Trade with insight. Trade with anticipation. (It's lonely at the top) مؤشر
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Live Footprint Center Frame📊 Live Footprint Center Frame
Live Footprint Center Frame is a footprint-style chart overlay designed to help traders study candle-by-candle volume behavior directly on the main chart. The script displays centered footprint frames around candles and breaks each candle into multiple price slots so volume structure can be viewed more clearly.
🔎 What It Does
The script visualizes estimated internal candle activity using lower-timeframe OHLCV data. It displays price-slot volume, delta behavior, POC, value area, imbalance markings, absorption-style highlights, delta bars, POC trail, and a compact HUD panel for quick market structure reading.
Key visual elements include:
• Footprint-style candle frames
• Price-slot volume ladder
• Estimated ask/bid style cell values
• Positive and negative delta coloring
• POC highlight
• Value Area highlight
• Imbalance arrows
• Absorption-style border highlights
• Small delta bar below candles
• POC trail between candles
• Compact HUD with ASK, BID, DEL, POC, VA, VOL, CVD, and bias information
🧠 General Logic
Live Footprint Center Frame uses lower-timeframe OHLCV data to approximate how volume is distributed inside each candle. The script divides the candle range into price slots, distributes lower-timeframe volume across those slots, and then calculates estimated delta, total volume, POC, value area, and balance information.
This is not true exchange-level bid/ask footprint data. It is an educational footprint-style approximation based on the data available through TradingView.
📌 Why It Is Useful
This tool can help traders study more than just candle open, high, low, and close. It gives a clearer visual view of where volume activity is concentrated inside the candle and how that activity changes from candle to candle.
It can be useful for observing:
• Where volume is concentrated
• How POC shifts between candles
• Whether delta supports or disagrees with price movement
• Where value area develops inside the candle
• Whether activity appears balanced or imbalanced
• Possible absorption-style areas
• Short-term volume structure around important price zones
🎨 Visual Reading Guide
Green-style cells show stronger positive delta behavior.
Red-style cells show stronger negative delta behavior.
Orange areas highlight the POC zone.
Blue-style areas highlight the value area.
Yellow borders can show absorption-style conditions.
Small delta bars below candles show candle delta direction and strength.
The HUD gives a quick summary of ASK, BID, DEL, POC, VA, VOL, CVD, and bias.
🧭 How To Use
1. Add the script to a clean chart.
2. Use intraday charts for clearer footprint-style reading.
3. Watch the POC area to see where the highest slot volume appears.
4. Compare candle delta with candle direction.
5. Use value area to understand where most candle activity is located.
6. Use imbalance and absorption highlights as context only.
7. Check the HUD for a quick summary of current candle conditions.
8. Combine this with market structure, support and resistance, liquidity zones, and proper risk management.
⚙️ Settings Overview
Lower timeframe precision controls the lower timeframe data used for the footprint approximation.
Closed bars to keep controls how many previous footprint candles remain visible.
Price slots per candle controls how many horizontal volume rows appear inside each candle.
Cell text controls whether cells show volume, delta, delta percentage, or estimated ask/bid style values.
Frame width adjusts the centered footprint frame width around each candle.
Live candle update allows the active candle footprint to update while the candle is forming.
POC trail connects POC movement between candles.
Value Area highlights the main volume zone.
Imbalance ratio controls imbalance sensitivity.
Absorption slot volume percentage controls absorption-style highlighting.
HUD settings control the compact panel position and visibility.
Color settings allow visual customization of bullish, bearish, neutral, POC, value area, frame, and delta elements.
⚠️ Limitations
This script uses TradingView-available OHLCV data and lower-timeframe calculations. It does not access true exchange order book data or true bid/ask footprint data on most symbols. Values can vary depending on symbol, timeframe, lower-timeframe availability, volume quality, and chart settings.
The live candle can update while it is still forming. Confirmed candles are more stable than the active candle.
✅ Educational Use Only
This script is provided for educational market analysis and visual order-flow style study. It does not provide financial advice, guaranteed results, or automatic trade decisions. Traders should use proper risk management and combine this information with their own analysis. مؤشر
Whale Liquidity and Absorption Profile [AlgoAlpha]🟠 OVERVIEW
The Whale Liquidity and Absorption Profile maps intrabar buying, selling, delta, and absorption activity into stacked horizontal profiles. It samples lower timeframe volume data inside each chart candle, then groups that activity into price bins to show where aggressive participation and absorption occurred across a configurable lookback range.
The script separates strong and weak activity using a percentile-based strength filter. It also builds a delta heatmap, absorption profile, historical absorption heatmap, and local absorption zones. Together, these components help traders identify where liquidity entered the market, where imbalance formed, and where price may react again.
🟠 CONCEPTS
Intrabar Sampling — Lower timeframe volume and directional data are requested using request.security_lower_tf() to reconstruct buying and selling activity inside each chart candle.
Strength Filter — Intrabar volume samples are ranked by percentile. Volumes above the selected percentile threshold are classified as strong activity while lower values are treated as weak activity.
Delta Profile — Buy volume minus sell volume calculated per price bin. Positive delta shows aggressive buying while negative delta shows aggressive selling.
Absorption Volume — Bullish volume occurring in upper wicks and bearish volume occurring in lower wicks. This is used to estimate where opposing liquidity absorbed incoming pressure.
Price Bins — The full price range inside the lookback is divided into vertical bins. All volume, delta, and absorption calculations are aggregated into these bins.
Absorption Peaks — Local highs in the absorption profile compared against neighboring bins. These areas are drawn as support and resistance zones.
🟠 FEATURES
Multi-Layer Volume Profile — Displays stacked buying and selling activity across price levels.
• Separates strong bullish, weak bullish, weak bearish, and strong bearish volume.
• Optional strong-only mode hides weak participation and normalizes the profile using only strong activity.
Delta Heatmap — Displays signed delta values directly inside each profile cell.
• Positive delta highlights dominant buying pressure.
• Negative delta highlights dominant selling pressure.
Absorption Profile — Aggregates wick-based absorption activity into a separate horizontal profile. (Buys at high wicks, Sells at low wicks)
Historical Absorption Heatmap — Creates rolling 5-bar heatmap snapshots to show where historical absorption accumulated over time.
Absorption Zones — Detects local absorption peaks and projects them across the chart as potential reaction areas.
Strong Activity Bubbles — Marks the strongest intrabar buying and selling events directly on price using percentile-ranked bubble tiers.
🟠 HOW TO USE
Load 2 instances of the indicator to bypass box drawing limits and use both the Absorption heatmap and the profiles.
Watch for stacked strong bullish volume combined with positive delta — this can show aggressive participation entering a price region.
Watch for stacked strong bearish volume combined with negative delta — this can show heavy selling pressure dominating a level.
Use absorption zones as areas where price previously encountered opposing liquidity — these zones may act as future reaction points.
Compare delta against absorption — strong positive delta with heavy upper-wick absorption can indicate trapped buyers or resistance.
Use the historical absorption heatmap to locate repeated liquidity interaction zones that price continues to respect over time.
Increase profile resolution for tighter price detail and reduce it for broader structural zones.
Enable strong-only mode to isolate high-participation liquidity events and remove weaker intrabar activity from the profile.
🟠 CONCLUSION
Whale Liquidity and Absorption Profile combines intrabar volume profiling, delta analysis, and wick-based absorption detection into a single structured framework. The indicator separates strong and weak participation while mapping where liquidity was absorbed across price levels. This gives traders a clearer view of imbalance, participation strength, and potential reaction zones inside the current market structure. مؤشر
Repricing Belt Engine [AGPro Series]Repricing Belt Engine
🔹 OVERVIEW
Repricing Belt Engine identifies qualifying displacement impulses and constructs ATR-scaled repricing belts around the impulse body, then tracks each belt's first-revisit lifecycle through three discrete outcomes: Held, Rejected, and Broken. The engine turns raw impulse candles into structured, evaluable reaction zones — giving traders a clean framework for studying how price behaves when it returns to the scene of a decisive move.
Unlike generic supply/demand or order block tools, this indicator does not simply mark impulse zones and leave them on the chart indefinitely. Every belt has a full state machine: Active → first qualifying touch → terminal outcome. A top-right status panel summarizes active belts, current belt context, 50-bar directional bias, and outcome counts so the chart context is always one glance away.
🔸 UNIQUE EDGE
Most displacement or supply/demand indicators stop at drawing a box. Repricing Belt Engine differentiates itself with four specific mechanics:
• ATR-scaled geometry — Belt width is normalized by ATR (not raw body size), producing consistently visible zones across volatility regimes and symbols. Optional "Auto (Body)" and "Body 70%" modes are available for traders who prefer tighter constructions.
• Three-outcome lifecycle model — Every belt resolves into one of five states (Active, Held, Rejected, Broken, Expired) based on penetration depth and close position. No more static zones cluttering the chart after price has decisively moved on.
• Depth-gated touch qualification — Wick-grazing does not trigger state transitions. A revisit must penetrate the belt by a configurable minimum depth (ATR-relative) before it counts, filtering noise from meaningful reactions.
• Excursion-tolerant hold detection — A belt can be marked Held even when price briefly dips beyond it, as long as the close respects the belt and the excursion stays within the Hold Tolerance band. This matches how institutional levels actually react in live markets.
🔹 METHODOLOGY
1. Impulse Qualification: A bar qualifies as an impulse when its body magnitude exceeds Impulse Threshold × ATR AND its body-to-range ratio is at least Min Body / Range Ratio. Both gates must pass — this filters out long-wick bars that look decisive but are not.
2. Belt Construction: On a qualifying impulse, a belt is drawn using the selected Width Mode. In ATR Scaled mode (default), the belt is centered on the impulse body midpoint and spans ±ATR Width Half-Span × ATR. A mid-line is drawn through the belt center.
3. Cooldown: A minimum bar gap (Cooldown Between Belts) is enforced between consecutive belt formations, preventing rapid clustering in extended trends.
4. Lifecycle Tracking: On every confirmed close, each active belt is evaluated:
• If close breaches the belt with excursion beyond Reject Tolerance → Broken
• If close recovers but excursion exceeded Hold Tolerance → Rejected
• If close respects the belt after a qualifying touch → Held
• If no resolution within Belt Max Lifetime bars → Expired
5. Active Cap: A maximum number of concurrent active belts is enforced (Max Active Belts). When the cap is reached, the oldest active belt is auto-expired to keep the chart focused on the current narrative.
🔸 SIGNALS & ALERTS
On-chart visuals:
• Bull/Bear belt zones with ATR-scaled width and mid-line
• Impulse origin markers (small triangles) anchoring each belt to its source bar
• State-colored labels at resolution: Held OK (state color), Broken X (opposite state color), Rejected (neutral, shown only in Detailed label mode)
• Faded rendering for resolved belts so the active story stays visually dominant
Top-right status panel:
• Active belt count
• Current belt context (side, position, held flag)
• 50-bar directional bias (Bull UP / Bear DN / Neutral)
• 50-bar outcome counts (Held / Rejected / Broken)
Alert conditions:
• Repricing Belt Formed
• Belt Touched (first qualifying revisit)
• Belt Held
• Belt Rejected
• Belt Broken
🔹 KEY INPUTS
Engine
• Impulse Threshold (x ATR) — impulse size gate
• Min Body / Range Ratio — decisive-close gate
• ATR Length — volatility normalization period
• Cooldown Between Belts — anti-clustering filter
Belt
• Width Mode — ATR Scaled / Auto (Body) / Body 70%
• ATR Width Half-Span — belt half-width in ATR units
• Belt Max Lifetime — auto-expiry age
• Max Active Belts — concurrent belt cap
State Thresholds
• Min Touch Depth (x ATR) — qualifying penetration
• Hold Tolerance (x ATR) — clean-hold excursion ceiling
• Reject Tolerance (x ATR) — rejected-vs-broken threshold
Visuals
• Label Mode — Clean / Detailed
• Panel Font Size, Label Font Size
• Show Impulse Markers toggle
🔸 HOW TO USE
1. Apply to any symbol and timeframe. The engine is timeframe-agnostic and self-calibrates via ATR.
2. Watch for new belt formations on displacement impulses. The impulse marker confirms the origin bar.
3. When price returns, observe the lifecycle resolution: Held reactions often mark continuation points; Broken reactions frequently signal structural shifts.
4. Use the 50-bar Bias readout for directional context — sustained one-sided belt formation suggests an active trend.
5. Combine with structure tools (swing highs/lows, trendlines) for confluence. The belt is a reaction zone, not a standalone entry system.
6. Tune Impulse Threshold per timeframe: lower values (1.5–1.7) for intraday, higher (1.8–2.2) for swing.
🔹 LIMITATIONS & TRANSPARENCY
• This indicator is not a strategy and does not generate buy/sell signals. It is an analytical visualization tool.
• Belt outcomes are historical observations, not predictions. Past Held/Broken patterns do not guarantee future reactions.
• Performance varies by symbol, timeframe, and market regime. Always test parameter settings on the instruments you trade.
• Very low-liquidity or gappy symbols may produce noisy impulses. Increase Impulse Threshold or Min Body Ratio for cleaner selection.
• The Max Active Belts cap intentionally limits chart information to keep focus on the current context — raise it only if your workflow benefits from longer belt history.
⚠️ RISK DISCLOSURE
This script is provided for educational and analytical purposes only. It is not financial advice and does not constitute a recommendation to buy, sell, or hold any asset. Trading involves substantial risk of loss. Always conduct your own analysis, manage risk responsibly, and never trade with capital you cannot afford to lose. Past performance of any pattern or setup is not indicative of future results.
Published as Public / Open-source under Mozilla Public License 2.0. مؤشر
Live HTF Volume POC - ChartDNAThis indicator displays a real-time volume profile with a dynamic Point of Control (PoC) line on any higher timeframe, from 15-minute sessions up to 3-month periods.
HOW IT WORKS
The script divides the selected timeframe's price range into configurable bins and accumulates normalized volume at each level. The bin with the highest volume is highlighted as the PoC. A stepping PoC line tracks how the highest-volume level shifts as the session develops in real time.
When a new HTF session begins, the previous session's profile is frozen on the chart, building a visual history of volume distribution across sessions.
FEATURES
- Flexible timeframe selector supporting intraday (15m, 30m, 1h, 2h, 4h, 8h, 12h) through higher timeframes (Daily, Weekly, Monthly)
- Dynamic PoC plotted as a stepline showing real-time PoC migration
- 8 profile color styles: Default, Monochrome, Blue, Cyan, Green, Red, Orange, Purple (all gradient-based)
- Adjustable bin count and PoC line width
- Clean overlay with minimal chart clutter
HOW TO USE
- Use on lower timeframes (e.g., 5m or 15m chart) with a higher session timeframe (e.g., 1D or 1W) to see where volume is concentrating
- The PoC level often acts as a magnet for price, useful for identifying support/resistance and mean-reversion zones
- Combine with your existing price action or order flow analysis
LIMITATIONS
- Volume data quality depends on your broker/exchange feed
- On very low-liquidity instruments, the profile may appear sparse
- The indicator uses request.security() with a offset and lookahead to detect HTF boundaries; this does not introduce future data
CREDITS
Based on "Real-Time HTF Volume Footprint" by BigBeluga (open-source, CC BY-NC-SA 4.0). Modified with added timeframe flexibility, multiple gradient color styles, PoC line customization, and UI cleanup.
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OutsiderEdge - Intrabar X-Ray⚠ This script uses request.footprint() and request.security_lower_tf(), which require a TradingView Premium plan or higher. The script will not load on Free or Plus plans.
Overview — What is X-Ray?
X-Ray is an open-source overlay indicator that dissects every candle into its sub-candle components, footprint data, effort-vs-result dynamics, and trap detection — all in one tool. Instead of guessing what happened inside a bar, X-Ray shows you.
It combines lower-timeframe candle decomposition with native footprint volume profiling, then layers on analytical modules that classify each bar's auction quality and warn for potential traps. Think of it as a bar-by-bar microscope that turns a single candle into a full story.
🔹 FEATURES
Tooltip Sub-Candle Viewer
Hover over any bar to see its internal structure: all lower-timeframe candles rendered as text-based charts inside the tooltip, with OHLC values, volume, bull/bear ratio, and summary statistics. Choose between a Colored or Black/White theme.
X-Ray Candle Coloring
Recolors your chart candles based on the bull/bear ratio of the sub-candles inside each bar. A bar that closed green but was internally 80% bearish sub-candles will show up differently than a uniformly bullish bar — exposing hidden weakness or strength at a glance.
Mini-Chart
Draws a real candlestick mini-chart to the right of the current bar, showing all lower-timeframe candles as actual visual candles with wicks and bodies. Gives you an instant intra-bar picture without switching timeframes.
Native Footprint Dashboard
A full volume footprint table rendered in the top-right corner using TradingView's native request.footprint() data. Displays buy and sell volume per price row, POC highlight, Value Area boundaries (VAH/VAL), delta, total volume, imbalance markers, and heatmap-style cell coloring — all without leaving your chart.
Effort vs Result Analysis
Classifies each bar by comparing volume effort against price result:
High Effort / Low Result — big volume, small body. Possible absorption or exhaustion.
Low Effort / High Result — small volume, large body. Vacuum/low-liquidity move.
Efficient Auction — delta-aligned, strong body, healthy volume. Clean price discovery.
Inefficient Move — delta opposed to direction with notable volume. Divergence warning.
Trap Detector
Scans each bar for structural trap patterns using delta, wick ratios, and body proportions:
Long Trap Risk — positive delta but bearish close with dominant upper wick.
Short Trap Risk — negative delta but bullish close with dominant lower wick.
Breakout Failed — wide range, tiny body, wicks dominate. Likely a fake move.
Continuation Likely — aligned delta, strong body, minimal wicks. Trend integrity intact.
Tutor Mode
An educational panel (bottom-right) that walks through each bar step by step: aggression direction, price reaction, imbalance stacking, POC location, volume context, and a final conclusion. Designed to help traders learn how to read order flow by seeing the reasoning broken down in real time.
🔹 HOW TO USE
Add X-Ray to your chart. Make sure your TradingView plan supports request.footprint() (Premium or higher).
Hover over any bar — the tooltip reveals the full sub-candle breakdown.
Check the candle coloring for quick internal sentiment at a glance.
Open the footprint dashboard (top-right) for row-by-row buy/sell volume, POC, and imbalance markers.
Look at the chart markers for Effort vs Result classifications and Trap signals.
Enable Tutor Mode to see a step-by-step explanation of the current bar's auction dynamics.
🔹 SETTINGS SUMMARY
① Tooltip: Bar width, number of candles, theme (Colored / B&W), marker style, OHLC/volume/summary toggles.
② X-Ray: Enable candle coloring, bull/bear/neutral colors.
③ Mini-Chart: Enable, offset, candle width, colors.
④ Footprint Dashboard: Enable, ticks per row, Value Area %, imbalance threshold, max rows, colors, totals, imbalance markers.
⑤ Data: Lower timeframe selection (Auto or manual), LTF calculation bars.
⑥ Effort vs Result: Enable, volume/body MA lengths, classification colors.
⑦ Trap Detector: Enable, trap type colors.
⑧ Tutor Mode: Enable, panel colors.
🔹 LOWER TIMEFRAME AUTO-DETECTION
When set to "Auto", X-Ray selects an appropriate sub-timeframe based on your chart:
Daily chart → 1h sub-candles
4h chart → 30m sub-candles
1h chart → 15m sub-candles
30m chart → 5m sub-candles
15m chart → 3m sub-candles
5m and below → 1m sub-candles
You can override this with a fixed timeframe in the Data settings.
🔹 GOOD PRACTICES
Use the tooltip and mini-chart to understand what happened inside a bar before making decisions based on its outer shape.
Effort vs Result works best when combined with context — a "High Effort / Low Result" bar at a key support level tells a very different story than one in the middle of a range.
Trap signals are most useful near structure (S/R, swing highs/lows, session opens). A trap in the middle of nowhere carries less weight.
Tutor Mode is great for learning but adds visual clutter — consider disabling it once you're comfortable reading the other modules independently.
The footprint dashboard shows the last bar only . Scroll through bars to compare footprints over time.
🔹 LIMITATIONS / DISCLAIMER
Requires TradingView Premium, Expert, or Ultimate plan for full functionality ( request.footprint() and request.security_lower_tf() ).
Footprint data availability depends on the exchange and instrument — not all symbols provide tick-level volume.
Sub-candle data is limited by the calc_bars_count parameter (default 300). Very high values may slow chart loading.
The Trap Detector and Effort vs Result modules use heuristic thresholds — they are probability tools, not certainties.
Trading involves substantial risk. This tool is for educational purposes only and is not financial advice. Past performance does not guarantee future results. You are solely responsible for your trading decisions and risk management.
Release Notes
v1.0 — Open-source release with: sub-candle tooltip viewer, X-Ray candle coloring, mini-chart, native footprint dashboard, Effort vs Result classification, Trap Detector, and Tutor Mode.
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