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
مؤشر
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.
مؤشر
مؤشر
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.
مؤشر
Footprint Interpreter [TechnicalZen]Footprint Interpreter
⚠ Requires TradingView Premium or Ultimate subscription. This indicator uses request.footprint() which is not available on free or Essential plans.
What This Indicator Does
The Footprint Interpreter reads real order flow — actual buy-side and sell-side execution data from TradingView's footprint engine — and translates it into a visual language that reveals what the market is doing beneath the surface of price action.
You do not need the footprint chart open. You do not need to read bid/ask ladders, decode volume profiles, or interpret heat maps. The indicator extracts the essential signal from footprint data and presents it as candles, a Kalman-filtered trend ribbon, and a regime engine — all in a single pane.
The core question it answers: "Where is the real money flowing, and is it accelerating or decelerating?"
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Why Footprint Data Matters
Price tells you where the market went. Volume tells you how much participated. But neither tells you who was the aggressor .
Every trade has a buyer and a seller. Footprint data decomposes each bar's volume into buy-side executions (orders hitting the ask) and sell-side executions (orders hitting the bid). The difference — the delta — reveals which side was actively pursuing fills.
This matters because:
Absorption — Price goes sideways while one side relentlessly absorbs the other's aggression. Invisible on a price chart. Clearly visible in footprint delta.
Pre-breakout accumulation — Before a move begins, aggressive buying or selling often builds in the delta while price hasn't yet responded. The Footprint Interpreter catches this divergence.
Exhaustion — A strong price trend continues but the delta weakens. The aggressive side is running out of conviction. The indicator's regime engine detects this deceleration.
Confirmation — Price breaks a level and the delta confirms with expanding aggression in the same direction. Confidence in the move increases.
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How It Works
The indicator runs three engines simultaneously, each operating on raw footprint data — no price-derived inputs.
Engine 1 — Footprint Delta (FPD) Candles
The cumulative footprint delta is computed bar by bar: each bar's buy volume minus sell volume, accumulated over time. This running total is detrended by subtracting a slow moving average so the display oscillates around zero rather than growing indefinitely.
The result is plotted as candles. Green candles mean the FPD rose during that bar (net buying pushed the cumulative total higher). Red candles mean it fell. The candle body directly represents the flow direction and magnitude.
Engine 2 — Dual Kalman Ribbon
Two Kalman-filtered lines track the FPD at different speeds:
Short KF (default length 20) — responsive, tracks recent flow shifts
Long KF (default length 80) — stable, tracks the underlying flow trend
When the short KF is above the long KF, the ribbon fills bullish. When below, bearish. The ribbon's width indicates the strength of the directional conviction — a wide ribbon means the fast and slow flow assessments strongly agree.
Engine 3 — Volume-Native DX/ADX Regime
The regime engine is a faithful translation of the classic Directional Movement system into the volume domain. Every component has a proper analog:
Directional Movement — Instead of comparing today's high vs yesterday's high, it compares today's buy volume vs yesterday's buy volume. Is buying expanding ? Is selling expanding? Only the winning side scores — the same competitive logic as the price-based original.
True Range — Instead of max(high-low, |high-close |, |low-close |), it uses max(totalVol, |buyVol-delta |, |sellVol+delta |). This captures both the bar's flow range and the gap from the previous bar's net settlement — detecting sudden flow reversals the way price TR detects overnight gaps.
DI+, DI-, DX, ADX — computed identically to the price version, but fed entirely by volume data.
The result is a four-state regime: Bull Strong, Bull Weak, Bear Strong, Bear Weak — telling you not just which side dominates but whether that dominance is accelerating or fading .
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Why Kalman Filtering — Not Hull, EMA, or SMA
The Kalman filter is fundamentally different from traditional moving averages. A moving average is a fixed recipe: take N bars, weight them, output a number. It has no concept of whether the data it is processing is noisy or clean, fast-moving or stable.
The Kalman filter is an adaptive estimator . It maintains two quantities internally:
State estimate — its current best guess of the true value
Uncertainty estimate — how confident it is in that guess
On every bar, it computes a Kalman gain that automatically balances between trusting the new measurement and trusting its prediction. When the data is stable, the gain drops and the filter smooths heavily. When the data shifts, the gain rises and the filter responds quickly.
This matters for footprint data specifically because volume flow is inherently noisier and burstier than price. A single large institutional order can spike the delta for one bar and vanish the next. Hull or EMA will whipsaw on these spikes. The Kalman filter recognizes the spike as high-uncertainty data and dampens its response — unless the spike is sustained, in which case it adapts.
The two tunable parameters — R (measurement noise) and Q (process noise) — give precise control over this behavior without changing the filter's length or structure.
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What It Reveals — Hidden Price Action
The most powerful use of this indicator is seeing what price alone cannot show:
Divergence Between Price and Flow
Price is rising but the FPD candles are falling or flat. This means the price advance is happening on declining aggressive buying — the move is running on fumes. The Kalman ribbon will begin to narrow and eventually flip before price confirms the reversal.
Pre-Breakout Energy
Price is consolidating in a tight range. The FPD candles begin trending directionally with expanding bodies. Aggressive flow is building on one side before the breakout occurs. The regime engine shifts from weak to strong. This gives the trader a heads-up — not a prediction, but evidence of building pressure.
Absorption Detection
Price pushes into a level and stalls. The FPD shows persistent negative delta despite bullish price action — sellers are absorbing every push. The Kalman short line begins diverging from the long line in the opposite direction of price. The regime stays weak or flips. The breakout attempt is likely to fail.
Trend Confirmation
Price breaks out and the FPD confirms: expanding candles in the same direction, both KF lines aligned and rising, regime at Bull Strong. This is the highest-confidence scenario — price and flow agree, and flow is accelerating.
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Reading the Indicator
The Candles
Green body — FPD rose during this bar (net buying dominated)
Red body — FPD fell (net selling dominated)
Body size — magnitude of the net flow shift
Position relative to zero — above zero = cumulative flow above its recent average; below = below average
The Kalman Ribbon
Thin line (Short KF) — fast flow tracker, colored by slope
Thick line (Long KF) — slow flow trend, colored by direction
Ribbon fill — bullish when short above long, bearish when below
Ribbon width — wider = stronger conviction, narrower = indecision or transition
The Dashboard
Regime — four-state flow regime (Bull Strong / Bull Weak / Bear Strong / Bear Weak)
Kalman — current Kalman ribbon direction
FPD Position — whether FPD is above or below the fast Kalman line
Vol DX — signed directional strength from the volume DM engine
Bar Delta — this bar's net buy minus sell volume
Buy / Sell Vol — raw buy-side and sell-side execution volumes
Imbalance — ratio of dominant side to weak side
FPD — cumulative footprint delta total
When footprint data is unavailable (instrument or plan limitation), the dashboard title turns red with a warning indicator.
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Key Settings
DX/ADX Period (default: 25)
Smoothing length for the directional movement engine. Higher values produce more stable regime readings at the cost of responsiveness.
Detrend Period (default: 50)
Length of the SMA subtracted from FPD for display centering. Does not affect any calculations — purely visual. Higher values show longer-term flow trends. Lower values focus on recent action.
Short KF / Long KF Length (default: 20 / 80)
Controls the responsiveness of the two Kalman-filtered lines. Shorter = more reactive. Longer = more stable. The gap between them determines how quickly the ribbon responds to flow reversals.
Measurement Noise R (default: 0.01)
How much the Kalman filter trusts new data versus its own prediction. Higher = smoother. Lower = more reactive to each bar.
Process Noise Q (default: 0.10)
How quickly the Kalman filter allows its internal state to change. Higher = adapts faster to regime shifts. Lower = more rigid model.
Ticks Per Row (default: 1)
Resolution of the footprint histogram. 1 = maximum precision. Increase for very high-volatility instruments if you encounter data limits.
Value Area % (default: 70)
Percentage of total volume that defines the Value Area in the footprint data. Industry standard is 70%.
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Requirements
TradingView Premium or Ultimate plan (required for request.footprint())
Instruments with footprint data available (most major futures, equities, and crypto pairs)
Pine Script v6
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What This Indicator Is Not
It is not a replacement for the footprint chart. It is a complementary interpretation layer. Traders who read raw footprint data will find additional context here. Traders who do not will gain access to footprint intelligence without needing to learn footprint chart reading.
It does not generate buy or sell signals . It provides directional regime information, flow momentum, and divergence detection. Trade decisions remain with the trader.
It does not predict price direction. It reveals the state of aggressive order flow and how that flow is evolving. What the market does with that information is never guaranteed.
It is not a volume indicator in the traditional sense. It does not use the standard volume series. It uses decomposed bid/ask execution data from TradingView's footprint engine — a fundamentally different and richer data source.
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Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, and it does not constitute a recommendation to buy, sell, or hold any financial instrument.
All trading involves risk. Past performance of any signal, regime detection system, or flow analysis mechanism does not guarantee future results. The regime states, Kalman-filtered trend readings, and flow analysis displayed represent a computational assessment of order flow data available through TradingView's footprint engine. They are not predictions and should not be treated as certainties.
Footprint data availability and accuracy depend on the instrument, exchange, and TradingView's data feed. The indicator requires a Premium or Ultimate TradingView subscription. Data gaps, exchange outages, or instruments without footprint support will produce missing or incomplete readings. The dashboard provides a visual warning when footprint data is unavailable.
No indicator, algorithm, or model can account for all market variables including liquidity events, news-driven gaps, exchange outages, dark pool activity, or sudden regime changes. Order flow data represents only the visible portion of market activity — significant volume may execute through channels not captured by footprint data.
Traders should always use independent risk management, position sizing, and their own judgment before entering any trade.
By using this indicator, you acknowledge that you are solely responsible for your own trading decisions and that the authors accept no liability for any losses incurred.
مؤشر
MTF Pure Delta Light [Zofesu]01 — Overview
What is MTF Pure Delta Light?
MTF Pure Delta Light is a minimalist overlay indicator that displays the volume delta of any chosen timeframe directly on your chart — as a compact, always-visible table widget. No separate pane, no clutter. Just the number and the trend direction.
Delta is the difference between buying and selling volume pressure for a bar. A positive delta means buyers dominated. A negative delta means sellers dominated. Combined with the HMA trend direction, you get an instant read on who is in control — on any timeframe — without leaving your chart.
BULLISH EXAMPLE
TF: D Delta Trend
+24.7K ▲
BEARISH EXAMPLE
TF: D Delta Trend
-19.4K ▼
Why "Light"?
This is the lightweight companion to the full Flow Oscillator. No separate pane, no cumulative history — just the current bar's delta value and trend direction from your chosen timeframe, always visible on the chart. Designed for traders who want context without complexity.
02 — Delta Calculation
How Delta Is Measured
The indicator uses the same dual-source delta standard as the full Flow Oscillator:
Footprint - Real bid/ask volume delta via fp.delta(). Used when your broker or data feed provides footprint data. Exact buying vs. selling volume per bar. High ✅
Elder CTI - Approximation using volume × (2×close − high − low) / range. Automatic fallback when footprint is unavailable. Works on all instruments and brokers. Good ⚡
The delta is then smoothed using an HMA (Hull Moving Average) to determine trend direction. HMA is used specifically because it minimises lag — the trend signal responds quickly to delta shifts without excessive whipsawing.
03 — Table Widget
Reading the Display
The table widget shows two values at all times:
Delta Value (left)
The raw delta of the selected timeframe bar. Green = positive (buyers dominated). Red = negative (sellers dominated). Formatted as volume shorthand (K, M).
Trend Arrow (right)
▲ Green — HMA delta trend rising. Buying pressure increasing.
▼ Red — HMA delta trend falling. Selling pressure increasing.
▬ Gray — No change. Neutral.
✅ Strong Bullish
Delta positive AND trend arrow ▲. Buyers in control and accelerating. Highest confidence bullish read.
⚠️ Divergence Warning
- Delta positive BUT trend arrow ▼ — buying is slowing.
- Delta negative BUT trend arrow ▲ — selling is slowing.
Potential shift incoming.
✅ Strong Bearish
Delta negative AND trend arrow ▼. Sellers in control and accelerating. Highest confidence bearish read.
➡️ Neutral
Delta near zero or trend arrow ▬. No dominant force. Avoid directional bias until one side takes control.
04 — Settings
Configuration Parameters
Group - Parameter - Default - Description
Core - Target Timeframe - D - Timeframe for delta calculation. Use standard TV notation: 1, 5, 15, 60, 240, D, W. Leave D for daily delta on any intraday chart.
Core - Trend Smoothing (HMA) - 5 - HMA length for delta trend direction. Lower = faster reaction, more signals. Higher = smoother, fewer but stronger signals.
UI - Table Position - Top Right - Position of the widget on the chart. Options: Top Right, Top Left, Bottom Right, Bottom Left, Middle Right, Middle Left.
UI - Text Size - Large - Widget text size. Small for compact charts, Large for readability at a glance.
UI - Background Opacity - 80 - Transparency of the widget background. 0 = fully transparent, 100 = fully opaque black.
Timeframe examples:
60 = 1 hour · 240 = 4 hours · D = Daily · W = Weekly
Recommended setup: If you trade H1 charts, set Target Timeframe to D. You get the daily delta context without switching charts.
05 — Use Cases
When and How to Use It
Higher timeframe context on intraday charts
Set Target Timeframe to D while trading on H1 or H4. The widget shows you whether the daily bar is currently dominated by buyers or sellers — without leaving your intraday view. Align your intraday trades with the daily delta direction for higher probability setups.
Quick confluence check
Before entering a trade, check the widget. If your price action setup is bullish but the delta is strongly negative with a ▼ trend — reconsider. If everything aligns — price structure, S/R, and positive delta ▲ — confidence increases.
Multi-indicator stack
MTF Pure Delta Light is designed to run alongside other indicators without occupying a separate pane. Pair it with Best MA for trend context and Flow Oscillator for cumulative delta history — all three together give a complete volume-delta picture.
Important: Delta is a confirmation tool, not a prediction tool. A positive delta tells you buyers dominated the last completed bar on your chosen timeframe — it does not guarantee the next bar will be bullish. Always use price structure and S/R as your primary decision framework. مؤشر
Institutional Footprint Divergence Engine🔹 Introduction
This indicator, the Institutional Footprint Divergence Engine, attempts to identify moments where price action and genuine order flow diverge — a condition that historically precedes reversals driven by smart money absorption and distribution. The core idea is this: if price makes a new swing high but the underlying buy-sell delta is contracting, the market is printing a higher high on less aggressive buying pressure, suggesting the move is being distributed into rather than genuinely accumulated. The inverse is equally meaningful at lows.
Unlike traditional divergence indicators that use derivative oscillators like RSI or MACD as the proxy for momentum, this script uses volume delta — the direct arithmetic difference between buying and selling volume at the bar level — sourced natively from TradingView's newly released request.footprint() function where a Premium or Ultimate subscription is active. On standard charts, the indicator falls back to a tick-estimated delta approximation. The distinction matters, and I'll cover precisely why throughout this description.
Every detected divergence is assigned a composite quality score from 0 to 100, computed across five weighted dimensions that assess the structural strength, volume context, cumulative delta alignment, and volatility regime at the moment of detection. Only divergences that clear a user-defined score threshold are displayed — filtering the noise that plagues most divergence tools.
🔹 The Premise — Why Delta Divergence Reveals Institutional Behavior
🔸 What volume delta actually measures
Every transaction in a liquid market has a buyer and a seller. Volume delta measures the net directional aggression of those transactions: it is the sum of volume that traded at the ask (aggressive buying) minus the volume that traded at the bid (aggressive selling) within a single bar. A positive delta bar means buyers were more aggressive. A negative delta bar means sellers were more aggressive.
This is categorically different from price direction. A bar can close higher while posting a negative delta — meaning price moved up, but sellers were the more aggressive counterparty throughout the move. This is the fingerprint of absorption: a large participant or group of participants quietly selling into rising price, absorbing aggressive buy orders without allowing the market to fall. They want retail to push price higher. They're using that momentum as liquidity to distribute their position.
Delta divergence is the systematic detection of this condition across swing structures.
🔸 The mechanics of absorption at swing highs
Assume price has been in an uptrend and just made a swing high at $4,200 with a delta of +850 contracts — strong buyer aggression confirming the high. Price retraces, then pushes up again to $4,215, printing a higher high. But this time, the delta is only +210. Price went higher. The aggressive buying volume did not.
What does this tell you? The move to $4,215 required proportionally far less buyer aggression than the move to $4,200. Two possibilities explain this: either sellers are absorbing the buying (distribution), or organic buying interest is fading and the move is increasingly resting on passive limit sell orders being consumed by declining buy-side momentum. Either way, the structural message is identical — the higher high is not supported by the order flow that created it, and the probability of continuation has deteriorated meaningfully.
This is the ICT and Smart Money Concepts concept of distribution rendered in order flow terms rather than price structure terms alone.
🔸 The symmetric argument at swing lows
At swing lows, the bullish divergence condition is: price makes a lower low, but the negative delta at that low is less negative than the prior swing low. Less aggressive selling at a lower price. This is absorption at the demand side — large buyers accumulating into weakness, absorbing retail sell orders without allowing price to collapse further. The lower low prints because they let it — they need the price to be there to fill their orders. But the delta tells you that sellers were unable to drive the same aggression they managed at the prior low.
Harris (2003), in his foundational text on market microstructure, describes this phenomenon as informed traders systematically positioning against the uninformed flow — using the uninformed participants' aggression as liquidity.
Cont, Stoikov & Talreja (2010), in their research on limit order book dynamics, demonstrate empirically that large passive participants consistently exploit periods of high aggressive flow imbalance to establish positions at favorable prices.
Delta divergence is not a leading indicator in the traditional sense. It is a coincident indicator of order flow context that becomes meaningful when paired with a confirmed swing structure.
🔸 Why native footprint data changes the calculus
Prior to January 2026, Pine Script had no access to true intrabar volume distribution. Every "delta" calculation in TradingView scripts was an estimate — typically assigning the bar's total volume directionally based on close position within the bar's range, or using up/down tick counting approximations. These methods are reasonable proxies but they introduce systematic errors: a bar that closes at its midpoint with heavy two-way activity looks identical to a quiet, directionless bar.
TradingView's request.footprint() function changes this entirely. It exposes the actual buy and sell volume recorded at each price level (row) within the bar — the genuine transaction-level data that footprint chart platforms like Sierra Chart and Bookmap have historically required separate subscriptions and data feeds to access. The delta returned by fp.delta() is not an estimate. It is the arithmetic difference between actual ask-side and bid-side transactions aggregated across the bar.
This is the first time this data has been natively programmable in Pine Script, and IFDE is built specifically around it.
🔹 How It Works
🔸 Footprint Data and the Delta Fallback
On a Premium or Ultimate TradingView account with a compatible symbol, request.footprint() returns a footprint object for each bar. IFDE calls fp.buy_volume() and fp.sell_volume() to get true directional volume, and fp.delta() for the bar's net delta. It also iterates every price row via fp.rows() and evaluates row.has_buy_imbalance() and row.has_sell_imbalance() — flagging bars where a disproportionate volume cluster exists at a specific price level, which often marks the precise price where institutional absorption occurred.
When footprint data is unavailable (standard account or non-supported symbol), the indicator falls back to a tick-estimated delta: up-close bars assign 100% of volume to the buy side; down-close bars assign 100% to the sell side; inside bars distribute proportionally based on close position within the range. This fallback is clearly flagged in the status label as ⚠️ ESTIMATED. The divergence logic functions identically in both modes — only the precision of the underlying delta changes.
The Ticks Per Footprint Row input controls the price granularity of the footprint: smaller values create more rows with finer resolution, larger values consolidate into fewer, broader rows. For index futures like ES and NQ, 4–10 ticks per row is typically appropriate. For crypto, you may need to experiment depending on the instrument's tick size.
🔸 Swing Pivot Detection
The indicator uses Pine's native ta.pivothigh() and ta.pivotlow() functions to identify confirmed swing highs and lows. The Swing Pivot Length input defines the lookback and lookahead symmetry of the pivot — a value of 10 means a bar must be the highest high within 10 bars on both sides to qualify as a pivot. Higher values find more significant structural swings but introduce more lag. Lower values are more responsive but noisier.
Critically, delta is sampled at the confirmed pivot bar using ta.valuewhen() — not at the current bar. This eliminates the most common repainting failure mode in divergence indicators: using the current bar's momentum reading to classify a past pivot. The delta value associated with each pivot is locked in the moment the pivot is confirmed.
🔸 Divergence Logic
Each time a new pivot high is confirmed, IFDE compares it against the previous confirmed pivot high. If the current price is higher but the current delta is lower, a bearish divergence is registered. The same comparison runs at pivot lows for bullish divergence, where current price lower and current delta less negative triggers the signal.
The Divergence Lookback setting controls the maximum bar distance between the two pivots being compared. Setting this too wide increases the chance of detecting structurally irrelevant comparisons — swings separated by 150 bars on a 5-minute chart may have no meaningful relationship. Setting it too tight misses legitimate multi-leg divergences. 40–60 bars is a reasonable starting point for most timeframes.
🔸 The ML Quality Score (0–100)
This is the engine's core differentiating feature. Every detected divergence is not displayed by default — it must first pass a composite quality score threshold. The score is calculated across five weighted dimensions:
Delta Magnitude is the most heavily weighted dimension by default (30%). It measures how extreme the opposing delta pressure is, normalised against the rolling maximum delta magnitude over the lookback window. A divergence where the delta is merely slightly less positive scores lower than one where the delta has completely reversed sign.
Volume Confirmation (25%) assesses whether total bar volume at the divergence pivot is above the 14-bar average. Low-volume divergences are structurally weaker — the absorption signal requires meaningful participation to be credible.
CVD Alignment (20%) checks whether the Cumulative Volume Delta — the running sum of all bar-level deltas, mean-reverted against its own moving average — is trending in the direction that supports the divergence. A bullish divergence at a price low carries far more weight when CVD has been quietly rising even as price made new lows.
Price Structure (15%) scores the magnitude of the price swing itself, relative to the current ATR. A divergence across a 0.5 ATR swing scores lower than one across a 2.5 ATR swing. Trivially small swings produce trivially meaningful divergence signals.
Regime Bonus (10%) applies a bonus or penalty based on the current volatility regime, described in detail below.
The weights are fully user-configurable in the 🤖 ML Score Weights input group. Shifting weight toward CVD Alignment, for example, will make the score more conservative and context-dependent. Shifting weight toward Delta Magnitude makes it more responsive to extreme single-bar order flow events. The scores are normalised internally so they always sum to 100 regardless of how you distribute the weights.
Only divergences scoring above the Min Quality Score threshold are displayed. The default of 55 is intentionally permissive to begin with. As you develop familiarity with the indicator on your instrument and timeframe, raising this to 65 or 70 will progressively filter toward only the highest-conviction setups.
🔸 Adaptive Regime Detection
The indicator compares the current 14-period ATR against its own simple moving average over the Regime Detection Period to classify the current volatility environment into three states: HIGH VOLATILITY, NORMAL, and LOW VOLATILITY.
In high volatility regimes, the score threshold is automatically scaled up by 20% — making it harder for a divergence to pass. This is because high-volatility environments produce frequent large delta swings that generate divergence signals with greater frequency but lower predictive value. The regime is tightening the filter precisely when noise is highest.
In low volatility regimes, the threshold is scaled down by 15%. Quiet, low-volatility markets are where institutional accumulation and distribution most commonly occurs under the radar — smaller delta contrasts carry more informational weight when total market activity is compressed.
The current regime and adjusted score floor are displayed in the status label in the top-left corner of the pane. A subtle background colour (green tint for low vol, red tint for high vol) is painted on the price chart to give continuous regime context at a glance.
🔸 The Pane Display
The indicator runs in its own pane below the price chart, containing three visual elements:
The delta histogram plots the smoothed EMA of bar-level delta as coloured columns — cyan for positive (net buying) and red for negative (net selling). The colour intensity scales with the magnitude of the delta relative to the recent maximum, so visually dominant bars correspond to the highest-conviction order flow readings.
The CVD deviation line in yellow shows the cumulative volume delta minus its moving average baseline. This is more useful than raw CVD for divergence context because it removes the secular trend in cumulative flow and focuses on relative shifts — making it easy to spot when CVD is rising or falling against price.
The zero line serves as the delta neutrality reference. Bars crossing from negative to positive delta, or vice versa, in the context of a divergence signal are particularly significant.
On the price chart, divergence lines connect the two pivot points being compared, with opacity scaling to score strength — higher-scoring divergences are rendered more vividly. Labels mark each divergence with its star rating (★ for score 55–69, ★★ for 70–84, ★★★ for 85–100) and the actual score value, along with whether live footprint data or tick estimation is in use.
🔹 Settings Reference
Swing Pivot Length — Controls pivot sensitivity. Lower = more signals, higher = more structural significance. Recommended: 8–15.
Divergence Lookback — Maximum bars between the two pivots being compared. Recommended: 30–75.
Min Quality Score — Score threshold below which divergences are hidden. Start at 55, tune upward as you calibrate to your instrument.
Ticks Per Footprint Row — Footprint granularity. Only relevant with live FP data. Tighter rows = more precision, more computation.
Delta Smoothing Period — EMA period applied to raw delta before divergence comparison. Smoothing reduces false triggers from single noisy bars. Recommended: 2–5.
CVD Baseline Length — Period of the SMA used to mean-revert the cumulative delta. Shorter = more responsive CVD; longer = smoother trend.
Alert Min Score — Score threshold for alert conditions. Set higher than the display threshold if you want alerts only for the strongest signals.
🔹 Closing Remarks
Delta divergence is one of the few conditions in technical analysis that has a genuinely defensible mechanical explanation rooted in market microstructure — it is not a pattern-matching heuristic but a direct observation of the imbalance between aggressive buying and selling pressure across a swing structure. The availability of native footprint data in Pine Script for the first time makes it possible to build this kind of tool without the estimations and approximations that have historically compromised order flow analysis within TradingView.
That said, this indicator is a probabilistic model, not a signal generator. A score of 90 does not mean the trade works. It means the order flow context at that divergence was unusually well-structured relative to the five dimensions measured. Markets can and do continue trending through well-formed divergences, particularly in strongly trending regimes where institutional participants are not distributing but rather re-accumulating on every pullback.
The most effective use of IFDE is as a confluence filter — a condition that must be present alongside your existing structural, session, or macro framework before you engage a level. A bearish divergence at a weekly resistance level, in a high-volatility regime, scoring 82, with live footprint data showing 7 sell imbalance clusters, is a meaningfully different proposition than a 56-scoring divergence on estimated delta at a randomly selected intraday high.
Use the score. Respect the regime. Verify the data source. The rest is your edge.
🔹 References
Market Microstructure & Order Flow
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
Cont, R., Stoikov, S., & Talreja, R. (2010). A stochastic model for order book dynamics. Operations Research, 58(3), 549–563.
Volume and Delta Analysis
Easley, D., & O'Hara, M. (1992). Time and the process of security price adjustment. Journal of Finance, 47(2), 577–605.
Easley, D., Hvidkjaer, S., & O'Hara, M. (2002). Is information risk a determinant of asset returns? Journal of Finance, 57(5), 2185–2221.
Institutional Order Flow & Smart Money
Chordia, T., Roll, R., & Subrahmanyam, A. (2002). Order imbalance, liquidity, and market returns. Journal of Financial Economics, 65(1), 111–130.
Grinblatt, M., & Keloharju, M. (2000). The investment behavior and performance of various investor types. Journal of Financial Economics, 55(1), 43–67. مؤشر
CandelaCharts - HTF Footprint Candles📝 Overview
The CandelaCharts - HTF Footprint Candles is a professional-grade visualization tool that overlays High-Timeframe (HTF) candles onto your current chart while integrating a volume footprint profile. This gives traders the ability to analyze structural behavior and internal volume distribution of institutional candles without switching timeframes.
📦 Features
Multi-HTF Visualization : Display up to 60 historical HTF candles as ghost overlays on the active chart.
Volume Footprint Integration : Sourced from a Lower Timeframe (LTF) using request.security_lower_tf for granular precision.
Multiple Footprint Modes :
- POC + Voids : Highlights the Point of Control and areas of low volume.
- Delta : Visualizes net buying vs selling pressure.
- Total Volume : Relative volume intensity at each price level.
- Voids : Specifically targets "gap" areas in price action.
New York Midnight Anchor : Option to anchor HTF candles to the NY 00:00 open for consistent institutional session analysis.
Dynamic Labels : Auto-updating labels for HTF period, remaining time until candle close, and candle timestamps.
⚙️ Settings
🕒 Timeframe Control (Hierarchical Logic)
To ensure the drawings display correctly, you must understand the relationship between the three timeframes involved:
HTF (Higher Timeframe) : This is the timeframe of the "Ghost Candles" (e.g., 4H, Daily). It must be higher than your chart period.
Chart Timeframe : The timeframe you are currently looking at.
LTF (Lower Timeframe) : Found in the HTF I group as "LTF". This is the source for the footprint calculation.
- 💡 Crucial Note : Your Chart Timeframe must be equal to or higher than the LTF setting. If you view a 1m chart but have LTF set to 5m, the footprint cannot be calculated.
📊 LOT (Bin Size) Control
The Lot setting (found in the HTF I group) controls the vertical resolution of the footprint.
Definition : It represents the percentage of price movement that defines a single footprint "bin" or level.
Configuration :
- Volatile Assets (e.g., BTC/NVDA) : Use a higher Lot value (e.g., 0.05 or 0.1) to avoid creating too many bins.
- Stable Assets (e.g., EURUSD/Stablecoins) : Use a lower Lot value (e.g., 0.005 or 0.01) for more granularity.
- ⚠️ Warning : Setting the Lot too small on a highly volatile asset can exceed TradingView's label limits, causing footprints to disappear or "evict" other chart annotations.
⚡️ Showcase
💎 Footprint Mode Presets
Point of Control (POC) : Highlights the single price level with the highest volume (Label count). Essential for spotting where the most trading activity occurred.
Delta : Displays net buyer vs seller pressure at each level. Positive delta shows buying aggression; negative delta shows selling aggression.
Total Volume : Visualizes the total activity at each price point, regardless of direction. Great for identifying high-activity ranges.
POC + Voids : A hybrid mode that shows both high-volume interest (POC) and low-volume gaps.
Voids : Specifically filters for "liquidity gaps"—price levels that the bar moved through so quickly that very little volume was transacted.
🚨 Alerts
The indicator includes on-chart warnings if your Timeframe settings are incompatible (e.g., Chart TF < LTF).
⚠️ Disclaimer
Trading involves significant risk, and many participants may incur losses. The content on this site is not intended as financial advice and should not be interpreted as such. Decisions to buy, sell, hold, or trade securities, commodities, or other financial instruments carry inherent risks and are best made with guidance from qualified financial professionals. Past performance is not indicative of future results.
مؤشر
TickCharts [crlmx]Volume-based candlestick chart - each candle represents a fixed dollar volume, rather than a time interval. A configurable bar statistics table shows delta, CVD, and volume breakdowns per candle. Reveals market participation pace, institutional activity, and regime shifts through candle formation speed.
Key Features
Dollar volume threshold candles (default $1M)
Tick-accurate volume via TradingView footprint API (Premium or above)
Bar statistics table with 6 configurable rows below candles
9 data types per row: Time, Volume, Delta, Buy, Sell, Delta %, Buy %, Sell %, Session CVD
Volume progress label showing dollar amount, threshold and percentage on the live candle
Streamlined input / UI brought to you by crlmx
Trading Applications
Volume candles compress during consolidation and expand during breakouts
Fast candle succession signals high participation; slow formation signals stalling
CVD tracks cumulative order flow direction across the visible range
Delta and CVD rows show buyer/seller dominance per candle
Recommended settings: Crypto (BTC/ETH): Candle Volume: $5M-$10M Index Futures (ES/NQ): Candle Volume: $1M-$2M
Commodities (Gold): Candle Volume: $500K-$1M
Version History
v0.42 (Latest - 07 Mar 2026)
Updated LTF Volume calculation to Footprint API
Added Bar statistics table with 6 configurable rows and 9 data types
Added row customisation مؤشر
Footprint Data Test [Zofesu]Overview
Verify your Data Integrity with Institutional Precision.
This diagnostic tool is a professional-grade utility designed to verify if your current broker and symbol provide real-time Footprint Data (Intrabar Volume). In the world of high-stakes trading, especially when scalping Nasdaq or BTC, your strategies are only as good as the data feeding them. This script ensures your "data fuel" is active before you rely on complex Order Flow or Delta-based indicators.
Why this is Original and Useful
TradingView's Public Library is filled with Order Flow indicators, but many traders fail to realize that their broker might not even provide the necessary tick-level data for these tools to function.
This script is unique because:
Direct Engine Access: It utilizes the latest Pine Script™ v6 request.footprint method to probe the exchange’s database directly.
Diagnostic Transparency: Instead of guessing why an indicator isn't plotting, this tool provides a clear "Green Light" status.
Educational Value: It bridges the gap between retail charting and institutional data requirements, helping traders understand the difference between simulated volume and real footprint distribution.
How it Works
The script attempts to request footprint data for the last 10 bars. In Pine Script™ v6, if the broker (e.g., certain CFD providers) does not support this data, the method returns na. The tool captures this state and translates it into a simplified UI.
Core Components
Status Table: A clean, non-intrusive UI element at the bottom center of your chart for instant diagnostics.
Data Active ✅: Confirms your broker provides real-time footprint/tick data. You can safely proceed with Order Flow and Volume Profile strategies.
No Data / Not Supported: Indicates the symbol or broker lacks the necessary granularity. Footprint-based indicators will not function correctly in this environment. Next to the indicator, at the top left, you will see a red exclamation point.
Strategic Application
Broker Verification: Compare data quality between different providers (e.g., IC Markets vs. Binance vs. CME).
Symbol Compatibility: Essential for testing Nasdaq (NQ), S&P 500 (ES), or Crypto pairs before deploying capital.
Data Subscriptions: If you see a red status on Indices, it often confirms the need for a "Real-Time Data Subscription" from the exchange. Switch time frames to see where the real data is. مؤشر
Institutional absorption scoreInstitutional Absorption Score (IAS)
Institutions don't buy all at once they accumulate slowly, hiding their footprint inside boring, low-range candles with high volume. By the time the breakout is obvious, they're already in. This indicator tries to catch them in the act by scoring how much of that absorption is happening right now.
How It Works
The score runs from 0 to 100 and checks for these things -> Is volume elevated but the candle barely moved? Is the range tight relative to recent volatility? Someone's holding price in a zone. Are lows quietly stepping up even though the chart looks sideways?. Each of these gets weighted and combined into a single score.
Reading the Score
Red means nothing interesting is happening, move on. Orange is worth a second look but don't act yet. Yellow is where you start watching closely absorption is building. Green means multiple things are aligning and institutions are likely active. Lime is the serious zone — this is where breakouts tend to come from, often when most traders are still bored.
Settings
Lookback Period — how far back the indicator looks to define "normal" volume and volatility. Increase it on noisy assets, lower it if you want faster reactions.
Volume Multiplier — sets the bar for what counts as high volume. If your asset is naturally volatile, push this higher so random spikes don't inflate the score.
Max Body/Range Ratio — how small the candle body needs to be. Lower values mean only very indecisive candles count, which is stricter but more precise.
Max Range % of ATR — filters for compressed candles. If price is moving freely, it's not being absorbed — this setting enforces that.
Higher Lows Lookback — how many bars back to check for a higher low structure forming underneath.
Score Smoothing — irons out bar-to-bar noise. Crank it up if the score feels jumpy, lower it if you want to catch signals earlier. مؤشر
Delta Strike: Order Flow Absorption & Momentum Confirmation**Delta Strike** is a professional-grade quantitative tool designed for traders who prioritize institutional logic over simple price action. It moves beyond traditional "buy/sell" indicators by dissecting the battle between **Passive Absorption** and **Aggressive Initiative** using underlying Order Flow data.
### 🛡️ The Core Philosophy: "Wait for the Trap, Trade the Escape"
Markets rarely reverse instantly. **Delta Strike** follows a rigorous two-step verification process to filter out noise and hunt for high-probability institutional footprints:
1. **Phase 1: Institutional Absorption (Left-Side Setup)**
The system identifies "Base Bars" where high volume and extreme Delta (passive buying/selling) occur, but price fails to continue. This indicates that a large player is absorbing the current move.
2. **Phase 2: Aggressive Strike (Right-Side Confirmation)**
We do not "catch the knife." Instead, the indicator monitors the next **N bars** for a confirmed strike. A signal is only triggered when price engulfs the base bar and is backed by a significant **Active Delta Percentage**, proving that the "absorber" has now become the "aggressor."
### 🚀 Key Technical Features
* **Dual-Cycle Volume Matrix**: Unlike standard indicators, Delta Strike analyzes volume across two lookback periods simultaneously (Short-term 20 & Long-term 50). It classifies setups into three categories:
* 🔥 **Dual-Cycle Convergence** (Maximum Strength)
* ⚡ **Short-term Spike** (Local Volatility)
* 🌊 **Macro Volume Surge** (Long-term Accumulation)
* **Active Delta Intensity Filter**: Every confirmation bar is evaluated for its "Net Win Ratio." By filtering out low-conviction, low-volume breakouts, it ensures you only follow moves with real institutional backing.
* **RSI Environment Guard**: Integrated RSI logic ensures that bottom absorption is only hunted in "Oversold" zones and top absorption in "Overbought" zones, significantly reducing whipsaws in sideways markets.
* **Validated SuperTrend (Delta-Sync)**: A modified SuperTrend algorithm that requires a "Delta Handshake." A trend flip is only considered valid if price and Delta move in the same direction, preventing "fake-outs" during low-liquidity periods.
### 📊 Clean & Actionable UI
* **Base Bar Highlight**: When a setup is confirmed, the script retroactively draws a **Yellow (Bullish)** or **Fuchsia (Bearish)** box around the original absorption bar.
* **Trace Lines**: Dashed lines connect the original institutional entry to your current entry point, providing immediate visual context for the trade's logic.
* **Momentum Rating (🐂/🐻)**:
* **3 Stars (🐂🐂🐂)**: Extreme Delta Strike (>20% Net Win).
* **2 Stars (🐂🐂)**: High Conviction Strike (>10% Net Win).
* **1 Star (🐂)**: Standard Confirmation.
### 🔔 Smart Alert System
Equipped with a fully customizable alert suite. You can set alerts for:
* **Absorption Confirmations** (Long/Short)
* **Validated SuperTrend Breakouts**
*Note: For the most accurate results, it is recommended to use "Any alert() function call" and set frequency to "Once Per Bar Close" to avoid repainting during intra-bar fluctuations.*
---
### How to use:
1. Look for the ** ** label and highlighted box.
2. Wait for the **Strike icons (🐂/🐻)** to appear within the N-bar window.
3. Combine with your existing Support/Resistance levels for optimal strike rates.
--- مؤشر
Smart Trader, Concentric Candles & Aristotelian Cycloids
Smart Trader, Episode 05
Concentric Candles & Aristotelian Cycloids
by Ata Sabanci
The Spark — How a 2,000-Year-Old Paradox Found the Charts
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It started with a film about Aristotle's Wheel
Paradox — a problem that puzzled mathematicians
for two millennia. The setup is deceptively
simple: two circles, one inside the other, share
the same center and roll together along a
straight line. The outer circle traces a distance
equal to its circumference. But the inner circle,
attached rigidly to the outer, also travels the
same distance — yet its circumference is smaller.
How?
The answer lies in slipping . The inner circle
doesn't truly roll — it is dragged . And as it
moves, a point on its rim traces a beautiful
curve called a curtate cycloid — a compressed
wave that never reaches the full height of the
outer wheel's standard cycloid.
The moment I saw those curves being drawn in the
film — the elegant, rhythmic arches of the
cycloid — a thought struck me: what if this
geometry lives inside price charts too? What if
each candle, with its High, Low, Open, and Close,
could be mapped onto a rolling circle — and the
resulting cycloid curves could reveal hidden
structure in market behavior?
That question became this indicator.
The Problem — Why Raw Charts Break Geometry
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A financial chart has two axes that measure
completely different things : the horizontal
axis counts bars (time) , the vertical axis
measures price (currency) . Drawing a circle on
such a chart is meaningless — it stretches and
distorts with every zoom or rescale. A "circle"
on a 1-minute chart looks nothing like the same
"circle" on a daily chart.
To draw real geometry on a price chart, both
axes must speak the same language . I needed a
scientifically rigorous way to convert between
time and price — not an arbitrary ratio, but one
derived from the market itself.
The Bridge — Volatility-Diffusion Normalization (σ√t)
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The answer comes from the σ√t scaling law in
financial mathematics. Under Geometric Brownian
Motion, the standard deviation of log-returns
scales as the square root of time:
std(Δ ln P) = σ × √Δt
This means 1 bar of time is equivalent to
σ_bar units of log-price . Once you know σ_bar,
both axes measure the same thing. Geometry
becomes invariant — it doesn't distort with
zoom, timeframe, or instrument.
For σ_bar, I use the Yang-Zhang volatility
estimator — the most statistically efficient
single-bar estimator in the literature. It uses
all four OHLC prices plus the overnight gap
between consecutive bars, combining three variance
components into one optimal estimate. After
extensive testing, a lookback of 20 bars proved
to be the best balance between responsiveness and
stability for geometric calibration.
Candle Selection — Finding the Dominant Voices
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Not every candle deserves a rolling wheel. The
indicator selects maxima candles — candles whose
H−L range is strictly larger than their immediate
chronological predecessor. These represent
volatility expansion events — moments when the
market spoke louder than the bar before it.
Two selection methods work in parallel:
1. Predecessor-Comparison (M0, M1): The scanner
walks backward from the basis candle. At each
step, it compares a candle's range to its
predecessor. When it finds one that is strictly
larger, that candle becomes a maxima. Two are
found this way.
2. Period Largest (M2): A separate scan finds
the single biggest candle (by H−L range) within
the last N bars (default 20). If it differs from
M0 and M1, it's appended as a third maxima — the
"biggest voice in the room."
Live vs. Closed Basis: The user can choose
whether the scanner starts from the live candle
(real-time, updates every tick) or the last closed
candle (stable, confirmed data). This affects both
the backward scan starting point and the CVCA
contact detection basis.
The Geometry — Three Concentric Circles per Candle
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Each selected maxima candle defines three
concentric circles in the normalized space — all
sharing the same center (the geometric midpoint
of the candle):
Outer Circle — diameter = H − L (full candle
range). This is the primary rolling wheel. A point
at its apex (the High) traces a standard cycloid .
Upper Wick Circle — diameter = H − max(O, C)
(upper wick). This inner wheel is dragged by the
outer — it slips along the baseline (Aristotle's
paradox). Traces a curtate cycloid with reduced
amplitude.
Lower Wick Circle — diameter = min(O, C) − L
(lower wick). Also dragged. Also traces a curtate
cycloid.
How the candle maps to circles:
⊚ Outer circle: diameter = H − L (full range)
→ traces a standard cycloid
▲ Upper wick circle: diameter = H − max(O,C)
→ traces a curtate cycloid
▼ Lower wick circle: diameter = min(O,C) − L
→ traces a curtate cycloid
All three share the same center (candle midpoint).
The outer circle is the driving wheel. The two
inner circles are dragged — they slip along the
rolling surface (Aristotle's paradox in action).
Drawing these curves in Pine Script was itself a
challenge — there's no native circle or parametric
curve function. By defining the normalized
coordinate space (where 1 bar = σ_bar units of
log-price) and using polylines with a hybrid
Newton-Bisection root solver to invert x(θ) → θ
at each bar index, I was able to render the
cycloid curves directly on the price chart with
high fidelity.
The parametric equations for each curve follow the
two-radius Aristotle's Wheel formulation:
x(θ) = R·θ − r·sin(θ+φ₀) + r·sin(φ₀)
y(θ) = R − r·cos(θ+φ₀)
Where R = outer rolling radius, r = traced
point's circle radius, and φ₀ = π (apex start,
clockwise rolling). The inverse transform
price = exp(lnLow + y_norm × σ_bar) maps the
normalized cycloid back to the price chart —
geometry anchored at the candle's Low.
On the chart, the curves appear as follows:
⊚ Outer Apex (y = 2R): highest cycloid point
▲ Upper Pin Apex (y = R + r_up): curtate peak
▲ Upper Pin Trough (y = R − r_up): curtate min
⊚ Outer Trough (y = 0): the rolling surface
= candle's Low price
The outer cycloid arches from Low up to 2R and
back. The pin cycloids oscillate within the outer
envelope with reduced amplitude — visible as
nested waves inside the main curve.
The Signal — Cycloid-Volume Contact Analysis (CVCA)
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Drawing beautiful curves is one thing. The real
question is: what happens when price touches a
cycloid curve?
The CVCA engine detects real-time intersections
between the live candle and all active cycloid
curves. When contact is detected, it runs a
7-axis prediction matrix that combines geometry
with volume analysis:
Axis 1 — Contact Direction: Did the body cross
the curve? Did a wick test it? Five states: Passed
Up, Passed Down, Stopped, Wick Bounce, Wick
Reject.
Axis 2 — Curve Type: Which circle generated
this curve? Outer (strongest), Upper Pin, or Lower
Pin.
Axis 3 — Curve Zone: Is the contact near the
Apex (peak), Trough (bottom), or Mid-range of the
cycloid?
Axis 4 — Volume Magnitude: How does current
volume at the contact price compare to the maxima
candle's volume at the same price? Amplified,
Proportional, or Depleted.
Axis 5 — Delta Character: How has the buy/sell
imbalance changed since the maxima candle?
Continued, Weakened, or Flipped.
Axis 6 — Absorption Detection: Is heavy
opposing volume being absorbed while price holds?
Buy Absorption, Sell Absorption, or None.
Axis 7 — Multi-Cycloid Confluence: How many
other cycloid curves pass through the same price
level? Single, Double, or Triple+ confluence.
All seven axes feed into a continuous scoring
engine using logistic-sigmoid soft-clamping — no
hard thresholds, no cliff-edge label flips. The
output is a directional probability P(↑), a
conviction score, and a behavior classification
(Strong Bounce, Breakout, Exhaustion, Absorption,
Battle Zone, Delta Flip, Confluence Wall, and
more).
When footprint data is available (TradingView
Premium/Ultimate), CVCA operates at tick-level
precision — comparing buy and sell volume at the
exact contact price row between the maxima candle
and the current candle. Without footprint, it
falls back to geometry-only analysis using the
first three axes.
Scientific Foundations
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This indicator draws on the following mathematical
and statistical frameworks:
• Geometric Brownian Motion — dS/S = μdt + σdW
— the σ√t diffusion scaling law that bridges
time and price axes.
• Yang-Zhang Volatility Estimator — Uses
O/H/L/C + overnight gaps; the most statistically
efficient single-bar σ estimator in the
literature.
• Rogers-Satchell Volatility — Drift-independent
variance component inside the Yang-Zhang
estimator.
• Aristotle's Wheel Paradox — Concentric circles
rolling together — the inner circle slips,
creating curtate cycloids.
• Cycloid Curves — Standard (outer) + curtate
(inner pin circles) — called the "Helen of
Geometry" by Galileo.
• Parametric Phase-Anchored Equations —
Two-radius cycloid:
x(θ)=Rθ−r·sin(θ+φ₀), y(θ)=R−r·cos(θ+φ₀).
• Hybrid Newton-Bisection Root Solver —
Numerical inversion of x(θ)→θ at each bar for
accurate curve rendering.
• Kyle-Obizhaeva Impact Law — σ√(Q/V) —
volume magnitude analysis for CVCA Axis 4.
• Easley-O'Hara PIN/VPIN — Delta as informed
trading proxy — CVCA Axis 5.
• Cont et al. (2014) OFI — Order Flow Imbalance
linear impact model — absorption detection in
CVCA Axis 6.
• Xu et al. (2019) MLOFI — Multi-Level Order
Flow Imbalance — confluence scoring for CVCA
Axis 7.
• Logistic Sigmoid Soft-Clamping — Continuous
scoring without cliff-edge thresholds — used
across all CVCA axes.
Architecture — How It All Connects
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The indicator processes data in the following
pipeline:
Stage 1: Raw Input
OHLC price data + volume arrive per bar.
Stage 2: Two Parallel Engines
→ Yang-Zhang σ_bar Estimator (Lookback = N):
Computes overnight variance, close-open
variance, and Rogers-Satchell variance.
Combines them into σ_bar.
→ Volume Engine (Geometric or Intrabar):
Splits total volume into buy/sell.
Stores per-bar for historical comparison.
Stage 3: Normalization
Converts axes: 1 bar = σ_bar, Y = ln(P) / σ.
Both axes now measure the same units.
Stage 4: Candle Selection
→ Predecessor-Comparison scan → M0, M1
→ Period-Largest scan → M2 (if unique)
Stage 5: Concentric Circles
Per maxima candle, 3 circles are defined:
⊚ Outer (H−L)
▲ Upper (H−max(O,C))
▼ Lower (min(O,C)−L)
Stage 6: Cycloid Math
Parametric equations + Newton-Bisection solver.
Phase φ₀ = π. Auto-revolutions extend curves
to the live bar.
Stage 7: Two Outputs
→ Polyline Render: 3 curves per Mi, bowl fill,
reference lines, geometric markers.
→ CVCA Engine: Contact detection, 7-axis
matrix, P(↑), conviction, behavior label.
Stage 8: Dashboard Table
Title + σ_bar info
Mi blocks (Offset, Length, Mid, Levels)
S/R Detection (Nearest Above/Below)
CVCA Contact Analysis (3-row layout)
Volume Engine (Buy/Sell/Delta/FP Status)
Settings Guide
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Core Settings
σ_bar Lookback Period: Rolling window for the
Yang-Zhang volatility estimator. Controls how many
bars are used to compute σ_bar — the bridge
between time and price axes. Larger values produce
more robust geometric calibration but respond
slower to regime changes. Default: 20.
Range: 1–2500.
Cycloid Display
Show Outer/Upper/Lower Traces: Toggle
visibility of each of the three concentric cycloid
curves. The Outer (H−L) is the primary rolling
wheel — the strongest geometric curve. Upper and
Lower Pin curves trace the wick circles and show
internal candle structure.
Show Period Largest Candle: Enables the
period-based maxima scanner (M2). Scans the last N
bars for the single biggest candle. If it already
matches M0 or M1, no duplicate is drawn.
Period Lookback Length: Number of bars to scan
for the period-based largest candle. Default: 20.
Range: 5–200.
Show Reference Lines: Draws horizontal dashed
lines at cycloid apex and trough levels — the
geometric S/R framework. Six individual toggles
control which levels are drawn: Outer Apex (2R),
Outer Trough (0), Upper Pin Apex/Trough, Lower
Pin Apex/Trough.
Curve Color — Above/Below Price: When the
Outer Apex level is above current close, all
curves for that maxima use the "Above" color
(default: orange). When below, they use the
"Below" color (default: cyan). This gives an
instant visual read of the curve's S/R context.
Volume Engine
Calculation Method: Two engines — Geometric
(estimates buy/sell from OHLC price action) and
Intrabar (uses lower timeframe tick data via
TradingView's ta library for precise
decomposition).
Intrabar Timeframe: Lower timeframe for
precise volume calculation. Only active in Intrabar
mode. 15S (15-second) recommended for most
instruments.
Calculation Basis: Current Candle uses live bar
data. Closed Candle uses only the last confirmed
bar — more stable, avoids intrabar noise.
Footprint & Contact Analysis
Show Contact Analysis (CVCA): Enables the
7-axis prediction engine in the dashboard. Detects
cycloid curve intersections and outputs directional
probability, conviction score, and behavior
classification. Requires TradingView
Premium or Ultimate for full footprint data; falls
back to Volume Engine data when footprint is
unavailable.
Dashboard Settings
Show Dashboard: Projects the full data
dashboard onto the main price chart. Shows σ_bar
info, all Mi blocks with reference levels, S/R
detection, CVCA contact analysis, and volume
metrics.
Dashboard Position: Four corners: Top Right
(default), Top Left, Bottom Right, Bottom Left.
Dashboard Language: English, Türkçe, or
العربية. Full localization of all labels,
tooltips, and natural-language sentences.
Show Mi Reference Prices: Expands each Mi
block in the dashboard to show all 6 reference
price levels (3 apex + 3 trough). Default: OFF to
keep the dashboard compact.
A Theory, Not a System — An Invitation to Explore
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This indicator is a theoretical exploration ,
not a proven trading system. The idea that
Aristotle's Wheel geometry maps meaningfully onto
price structure is a hypothesis — one that I
find compelling enough to build, share, and test
publicly, but one that requires far more data,
analysis, and community scrutiny to validate or
refute.
I chose to publish it on TradingView precisely
because this platform is a living laboratory —
constantly moderated, reviewed, and challenged by
a global community of traders, developers, and
analysts. If the cycloid geometry holds predictive
value, this community will find it. If it doesn't,
this community will expose it.
Use this indicator as a research tool, not as a
signal generator. Combine it with your own
analysis. Question its assumptions. Test it across
instruments and timeframes. And if you discover
something — share it.
The mathematics is beautiful. Whether the market
agrees is an open question.
مؤشر
Absorption BubblesSUMMARY
This indicator visualizes absorption events by plotting bubbles on candle wicks where volume activity suggests one side of the market is absorbing the other’s pressure. Instead of raw volume, the script normalizes activity against a rolling standard deviation defined by the Lookback Period. Bubbles appear on upper or lower wicks depending on whether buyers or sellers are absorbing pressure. The goal is to highlight whether aggressive orders are being accepted or absorbed at key price points.
METHODOLOGY
Absorption occurs when one side of the market absorbs aggressive orders from the other, preventing continuation. The script measures normalized volume against a user‑defined threshold to filter out weaker signals.
Green bubbles on upper wicks → Selling absorption (buyers push price up, sellers absorb the buying).
Red bubbles on lower wicks → Buying absorption (sellers push price down, buyers absorb the selling).
Red‑colored bars highlight candles where large volume is concentrated inside the body, signifying aggressive selling activity.
Green‑colored bars highlight candles where large volume is concentrated inside the body, signifying aggressive buying activity.
The Lookback Period controls how many bars are used to calculate the rolling standard deviation of volume, letting traders adjust sensitivity to recent vs. longer‑term activity. Optional significant volume lines extend forward, marking areas where absorption was strongest.
FUNCTIONS
Normalized volume detection using rolling standard deviation
Adjustable Lookback Period for volume normalization
Dynamic bubble plotting on candle wicks (size scales with absorption strength)
Separate visualization for buying vs. selling absorption
Alerts for buying absorption, selling absorption, or any absorption event (only at bar close)
Bar coloring when large absorption occurs inside candle bodies
APPLICATION
Setup: Add the script to any chart and timeframe. Adjust the Absorption Threshold to filter out weaker bubbles and the Lookback Period to control how volume normalization is calculated. Red bubbles highlight buying absorption, often signalling potential price pivots - price can often go upwards from this. Green bubbles mark selling absorption, reflecting resistance to upward moves - price may go downwards from this.
Interpretation:
Green bubbles on upper wicks = sellers absorbing buying pressure.
Red bubbles on lower wicks = buyers absorbing selling pressure.
Larger bubbles = stronger absorption relative to recent volume.
Settings & Use:
Raising the Absorption Threshold filters out smaller bubbles, leaving only significant absorption events.
Changing the Lookback Period alters how “normal” volume is defined — shorter periods make the script more sensitive, longer periods smooth out noise.
Alerts can be set for buying absorption, selling absorption, or any absorption event, and they only trigger at bar close to avoid noise. مؤشر
Orderblock Footprints [AlgoAlpha]🟠 OVERVIEW
This script highlights orderblocks and then drills into what actually trades inside them. Zones are created only after an abnormal directional impulse, measured with a z-score on consecutive candle bodies, so the orderblocks are tied to real expansion rather than simple pivots. Once a zone exists, the script overlays lower-timeframe volume footprints inside the candle when price trades back into that zone. The goal is to show not just where an orderblock sits, but whether price is being accepted or absorbed when it is revisited.
🟠 CONCEPTS
Orderblocks are detected after extreme bullish or bearish impulses. The script tracks consecutive body movement up or down, normalizes that distance with a rolling z-score, and only triggers when the move is statistically large. The last opposite candle before that impulse defines the orderblock range. These zones then extend forward until they are either mitigated by price closing through them or they expire by age.
Inside an active zone, the script switches to a lower timeframe and builds a footprint-style profile for each bar. Each candle is split into price rows, counting time-at-price and volume delta. Positive and negative delta are colored separately. Absorption is flagged when opposing delta prints appear in the wick that rejects the zone. In practice: the impulse defines context ; the footprint shows interaction .
🟠 FEATURES
Separate bullish and bearish zones with automatic extension
Volume split inside each zone candle (up vs down volume)
Lower-timeframe footprint with TPO-style rows and delta gradient
Absorption detection using opposing delta in rejection wicks
Alerts for zone creation and absorption events
🟠 USAGE
Setup : Add the script to your chart. It works on any market and timeframe. The lower timeframe for footprints is fixed at 5 minutes, so higher chart timeframes show clearer structure. Use the Z-Score Window to control how strict impulse detection is and Max Box Age to limit how long old zones stay on the chart.
Read the chart : Bullish orderblocks are created after strong upward impulses and are invalidated when price closes below them. Bearish orderblocks are created after strong downward impulses and are invalidated when price closes above them. When price trades inside a zone, footprint rows appear. Green-tinted rows show positive delta; red-tinted rows show negative delta. Absorption labels appear when opposing delta prints into a rejecting wick.
Settings that matter : Increasing the Z-Score Window makes orderblocks rarer but more significant. Disabling Prevent Overlap allows stacked zones if you want to study clustering. Adjusting Rows per bar changes footprint resolution—lower values are cleaner, higher values show more detail but use more objects.
مؤشر
