Indicator
Inducement Engine Liquidity Targets [MarkitTick]💡 The financial markets operate as a continuous auction process, constantly seeking liquidity to facilitate large transactions. We have developed a comprehensive analytical tool designed to systematically map these liquidity zones, specifically focusing on the concept of inducement (IDM). This tool tracks market structure in real-time, identifying areas where market participants are structurally trapped, and highlights the subsequent liquidity sweeps that often precede significant directional moves. By mapping these pivot points and applying stringent confluence filters, we provide a structured approach to analyzing price action without relying on lagging, derivative-based oscillators.
✨ Originality and Utility
Standard structural tools often map higher highs and lower lows but fail to categorize the internal liquidity that resides between these structural bounds. Our tool distinguishes itself by isolating inducement points—short-term swing highs or lows that form within an active leg of market structure.
Instead of treating all pivots equally, we categorize them based on their relationship to the overarching trend. Furthermore, this tool does not simply plot historical data; it actively tracks pending liquidity pools and waits for their invalidation to derive actionable zones.
This utility is enhanced by a built-in risk management engine that dynamically calculates entry, stop loss, and multiple take profit voids based on market volatility, offering a complete, end-to-end framework directly on the chart. The integration of volume volatility, exhaustion profiling, and structural imbalances creates a unified, logical system rather than an arbitrary assembly of unrelated indicators.
🔬 Methodology and Concepts
● The Mechanics of Inducement
Market Structure Tracking: We utilize an advanced, non-repainting pivot identification algorithm that maps confirmed structural highs and lows. This establishes the primary directional bias and prevents the plotting of unconfirmed future data.
Pending Liquidity Generation: When an internal pivot forms within the established structural range, it is categorized as a pending inducement point. These represent areas where early market participants place stop losses, creating pools of concentrated liquidity.
The Liquidity Sweep: The core engine monitors price action for the precise moment these inducement points are breached. A sweep indicates that the pending liquidity has been consumed, providing the fuel required for a potential reversal or trend continuation.
Multi-Layered Confluence: A sweep alone is insufficient for validation. We evaluate the trigger against several strict conditions. We require alignment with a higher timeframe trend, ensuring we trade with the dominant macro flow. We also measure volatility against a moving baseline to filter out low-momentum chop.
Exhaustion and Imbalance: Finally, we assess the structural integrity of the move by checking for price exhaustion through rejection wicks and the presence of underlying fair value gaps (FVGs) that validate the momentum.
🎨 Visual Guide
● Chart Elements
Pending IDM Lines: Dotted lines projecting horizontally from internal pivots. These represent untouched liquidity pools waiting to be swept.
IDM Sweep Labels: Distinct visual markers displaying "IDM ↑" and "IDM ↓". These appear exactly when price sweeps a pending liquidity level, signaling a potential reaction.
Entry Zones (Black Boxes): A solid, dark zone originating at the trigger point, highlighting the exact entry threshold for the setup.
Stop Loss Zones (Red Boxes): A colored zone delineating the maximum risk threshold, visually adapting to the current structural invalidation point and volatility padding.
Take Profit Voids (Blue/Cyan Boxes): A series of progressively lighter colored zones representing Take Profit 1, 2, and 3. These voids illustrate the projected risk-to-reward extensions based on the initial risk profile.
Target Lines: Horizontal dashed and dotted lines projecting the exact price levels for the entry, stop loss, and multiple take profit targets.
● The Live Dashboard
Header: Displays the active ticker and timeframe configuration.
Bias: Indicates the overarching structural trend (Bullish, Bearish, or Neutral).
Last IDM: Shows the direction of the most recently swept liquidity pool.
Coordinates: Displays the exact numerical price levels for Entry, TP1, TP2, TP3, and SL.
Duration Tracking: Tracks the exact number of bars since the last confirmed Bull or Bear IDM sweep, offering a measure of setup maturity.
Pending Count: A live counter of the currently active, untouched inducement points on the chart.
📖 How to Use
Observe the dashboard to determine the active market bias and monitor the creation of pending IDM levels.
Wait for price action to sweep a pending IDM line. The appearance of an IDM sweep label serves as the primary catalyst.
Verify that the dashboard registers the setup, meaning all selected smart filters (Time, HTF, Volatility, FVG) are aligned.
Utilize the dynamically plotted Entry, Stop Loss, and Take Profit boxes to structure your position sizing and risk profile.
The projected target voids can be used to manage risk or scale out of positions sequentially as price moves into higher extensions.
Avoid using this indicator in sideways market conditions to prevent false entries.
⚙️ Inputs and Settings
● Core Configuration
Swing Length: Adjusts the sensitivity of the pivot detection. Higher values filter noise, identifying more significant structural points.
ATR Length: Modifies the lookback period for volatility calculations used in risk mapping.
Max IDM Memory: Controls the historical limit for tracking untouched liquidity pools.
● Entry and Risk Parameters
ATR-Adaptive SL: When enabled, we pad the structural stop loss with an average true range multiplier to account for market noise.
ATR SL Mult: Defines the precise multiplier used for the adaptive padding.
Target Multipliers (TP1, TP2, TP3): Defines the precise risk-to-reward ratios for the sequential take-profit voids.
Line Toggles: Independent toggles to show or hide the Entry, Stop Loss, and Target lines to keep the chart clean.
● Smart Filters
Session Time Filter: Restricts signal generation to specific trading windows, avoiding low-liquidity periods.
HTF Trend Alignment: Enforces agreement with a moving average calculated on a higher timeframe.
HTF Timeframe & Length: Configures the resolution and period for the higher timeframe trend filter.
FVG Confluence: Requires a recent fair value gap to validate the momentum behind the setup.
Rejection Quality: Filters setups ensuring the trigger bar closes with distinct rejection characteristics.
Volatility Chop Filter: Suppresses setups when current volatility is below its historical average.
● Visuals and Alerts
Color Configurations: Extensive user-defined color inputs for Bull/Bear markers, Entry Fills, Target zones, and text elements.
Webhook Actions: Dedicated string inputs to format dynamic alert payloads, allowing seamless integration with third-party execution platforms.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
Auction Market Theory: Our architecture is deeply rooted in Auction Market Theory, which postulates that price discovery relies on the continuous search for liquidity. By mapping inducement, we are effectively modeling the algorithmic search for counter-party volume. Markets move from areas of high liquidity to low liquidity, and tracking these pools provides a probabilistic edge.
Mean Reversion within Structural Bounds: The identification of internal sweeps relies on statistical mean reversion. When price deviates aggressively to sweep a pivot, it creates a temporary state of overextension. The subsequent reaction is a reversion to the mean of the macro structure, propelled by the trapped liquidity that was just consumed.
Volatility-Adjusted Risk Modeling: The integration of the Average True Range (ATR) for stop-loss padding utilizes basic heteroskedasticity principles. Financial time series exhibit volatility clustering; by padding risk dynamically, the model adapts to the current state of market variance rather than relying on static, arbitrary tick values that fail in highly volatile environments.
Volume Spread and Exhaustion: The rejection filter incorporates elements of Volume Spread Analysis. A sweep accompanied by price rejection signifies absorption—a scenario where the effort to push price beyond a level is met with overwhelming counter-force, statistically validating the structural trap.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicator
Auto TrendLine Intelligence [BOS+CHoCH+FVG] [Rehan Khanani]================================================================
AUTO TRENDLINE INTELLIGENCE
================================================================
Auto TrendLine Intelligence is a professional-grade, institutional-level technical analysis indicator built for traders who think and operate like smart money. It combines automatic trend line detection with full Smart Money Concepts — BOS, CHoCH, and FVG — into one clean, powerful overlay.
This is not just a trend line tool. It is a complete market structure analysis system that tells you what the market is doing, why it is doing it and exactly where to enter, exit, and place your stop-loss.
================================================================
HOW IT WORKS
================================================================
The indicator runs five analytical engines simultaneously on every bar:
ENGINE 1 — AUTOMATIC TREND LINE DETECTION
The indicator automatically detects the most recent swing highs and swing lows using a pivot-based algorithm, then draws two types of trend lines in real time without any manual input:
Major Trend Lines (solid): Connect significant swing highs and swing lows using the full pivot lookback length. These represent the dominant structural trend and carry the highest weight for trading decisions.
Minor Trend Lines (dashed): Connect shorter-term pivots using a reduced lookback period. These represent intraday or near-term structure and are useful for precise entry timing.
Both lines update automatically as new pivots form. No manual drawing, no redrawing, no guesswork.
ENGINE 2 — BREAK OF STRUCTURE (BOS)
A Break of Structure is a continuation signal. It occurs when price convincingly breaks above a previous swing high (bullish BOS) or below a previous swing low (bearish BOS), confirming that the current trend is likely to continue.
Bullish BOS: price closes above the last confirmed swing high — signals trend continuation to the upside.
Bearish BOS: price closes below the last confirmed swing low — signals
trend continuation to the downside.
BOS labels appear directly on the chart at the point of the break.
ENGINE 3 — CHANGE OF CHARACTER (CHoCH)
A Change of Character is a reversal signal — the first indication that the current trend may be ending and a new one beginning. It is the most important signal in Smart Money analysis.
Bullish CHoCH: in a confirmed downtrend (lower highs, lower lows), price breaks above a recent lower high for the first time — this signals a potential reversal to the upside.
Bearish CHoCH: in a confirmed uptrend (higher highs, higher lows), price breaks below a recent higher low for the first time — this signals a potential reversal to the downside.
CHoCH labels are clearly marked on the chart and distinguished from BOS.
ENGINE 4 — FAIR VALUE GAP (FVG)
Fair Value Gaps are imbalance zones left behind by aggressive institutional buying or selling. Price frequently returns to these zones to rebalance before continuing in the original direction.
Bullish FVG: a gap between the high of the candle two bars ago and the low of the current candle — shown as a teal shaded zone.
Bearish FVG: a gap between the low of the candle two bars ago and the high of the current candle — shown as a red shaded zone.
FVG boxes automatically disappear when price returns to fill the gap (mitigation), keeping the chart clean at all times.
ENGINE 5 — LIQUIDITY ZONES
Equal highs and equal lows represent areas where institutional stop orders and limit orders are clustered. The indicator detects when two recent pivot highs or lows are at approximately the same price level and marks them with a dotted yellow line — highlighting where liquidity is likely to be swept before a major move.
================================================================
ENTRY SIGNAL SYSTEM (MULTI-FILTER CONFLUENCE)
================================================================
LONG and SHORT entry signals are generated only when ALL of the following conditions align simultaneously — ensuring institutional-grade signal quality:
For a LONG signal:
1. Price touches or approaches the support trend line within ATR buffer
2. Price is above the EMA 200 (bullish market bias confirmed)
3. RSI is not in overbought territory (momentum not exhausted)
4. Volume is above the 20-period average (institutional participation)
5. Candle closes bullish (price accepts support level)
For a SHORT signal:
1. Price touches or approaches the resistance trend line within ATR buffer
2. Price is below the EMA 200 (bearish market bias confirmed)
3. RSI is not in oversold territory (momentum not exhausted)
4. Volume is above the 20-period average (institutional participation)
5. Candle closes bearish (price rejects resistance level)
This five-layer confluence model ensures you only take trades where structure, trend, momentum, and volume all agree.
================================================================
AUTOMATIC TP1 / TP2 / SL LEVELS
================================================================
Every entry signal automatically plots:
Stop Loss: placed below/above the entry using ATR multiplier (default 1.5x ATR)
Take Profit 1 (TP1): at 50% of the full RR target — for partial exits
Take Profit 2 (TP2): at full Risk:Reward ratio target (default 2:1 RR)
All three levels are drawn as dashed lines directly on the chart with
exact price labels so you never have to calculate manually.
================================================================
INSTITUTIONAL DASHBOARD — 12 DATA POINTS
================================================================
A professional real-time dashboard is displayed in the corner of your
chart showing:
1. Market Bias — BULLISH or BEARISH based on EMA 200 position
2. Market Structure — Higher Highs/Lows | Lower Highs/Lows | Consolidation
3. EMA Stack — Bullish Stack | Bearish Stack | Mixed
4. RSI Reading — Exact value + zone (Overbought / Oversold / Neutral)
5. Volume Status — High Volume | Above Average | Below Average
6. ATR Value — Current ATR for volatility awareness
7. Support TL — Projected support trend line price at current bar
8. Resistance TL — Projected resistance trend line price at current bar
9. Fair Value Gap — Bullish FVG | Bearish FVG | None
10. SMC Event — Latest BOS or CHoCH event detected
11. Signal Status — LONG ENTRY | SHORT ENTRY | WATCHING
Dashboard position is fully adjustable: Top Right, Top Left, Bottom Right, or Bottom Left.
================================================================
FULL FEATURE LIST
================================================================
Automatic Features (Zero Manual Work Required):
- Auto major trend lines from pivot highs and lows
- Auto minor trend lines from shorter-term pivots
- Auto BOS detection and labeling
- Auto CHoCH detection and labeling
- Auto FVG zone drawing and auto-removal on mitigation
- Auto liquidity zone detection (equal highs / equal lows)
- Auto TP1, TP2, SL calculation and plotting on every signal
- Auto dashboard with 11 live market readings
Filters and Controls:
- EMA 200 trend direction filter
- EMA 50 for stack analysis
- RSI momentum filter (adjustable OB/OS levels)
- Volume spike filter (adjustable multiplier)
- ATR-based signal sensitivity buffer
- All filters individually toggleable
Visual Controls:
- Major and minor trend line toggle
- Individual color pickers for all line types
- Line width control (1 to 5)
- Extend lines right toggle
- FVG transparency control
- Dashboard position selector
Alert Conditions (9 Total):
1. LONG Entry Signal
2. SHORT Entry Signal
3. Any Entry Signal
4. Bullish BOS
5. Bearish BOS
6. Bullish CHoCH
7. Bearish CHoCH
8. Bullish FVG detected
9. Bearish FVG detected
================================================================
SETTINGS GUIDE
================================================================
Group 1 — Trend Line Settings
Pivot Lookback Length: Controls how significant a swing must be to form a trend line. Default is 10. Lower values (5-7) give more frequent lines suitable for scalping. Higher values (15-20) give fewer but stronger structural lines suitable for swing and position trading.
Show Major Trend Lines: Toggle the primary solid trend lines on/off.
Show Minor Trend Lines: Toggle the secondary dashed trend lines on/off.
Extend Lines Right: Extends all lines to the right edge of the chart.
Major Line Width: Visual thickness of major trend lines (1 to 5).
Color pickers: Customize all four line types independently.
Group 2 — Smart Money Concepts
Show BOS: Toggle Break of Structure labels on/off.
Show CHoCH: Toggle Change of Character labels on/off.
Show FVG: Toggle Fair Value Gap boxes on/off.
Show Liquidity Zones: Toggle equal high/low dotted lines on/off.
FVG Transparency: Adjust how opaque or subtle the FVG boxes appear.
Group 3 — Signal and Entry Settings
ATR Length: Period for ATR calculation (default 14).
ATR Buffer Multiplier: How close price must come to the trend line to
trigger a signal. Lower = stricter, higher = more relaxed.
Stop Loss ATR Multiplier: Distance of SL from entry in ATR units.
Risk Reward Ratio: Multiplier applied to the SL distance to set TP2.
Show TP/SL on Chart: Toggle the TP1, TP2, SL lines on/off.
Group 4 — Volume and Momentum Filter
Enable Volume Filter: When on, signals only fire on above-average volume.
Volume Spike Multiplier: Threshold multiplier over 20-period volume MA.
Enable RSI Filter: When on, blocks signals in exhausted momentum zones.
RSI Length, Overbought, Oversold: Standard RSI parameters.
Group 5 — Dashboard
Show Dashboard: Toggle the entire dashboard panel on/off.
Dashboard Position: Place it in any corner of the chart.
================================================================
HOW TO USE — STEP BY STEP
================================================================
Step 1 — Check the Dashboard
Before looking at any signal, check the dashboard. Confirm Market Bias, Structure, and EMA Stack all point in the same direction. Trade only when at least two of the three agree.
Step 2 — Identify the Trend Lines
Watch where the major trend lines are relative to current price. The support line (teal/blue) and resistance line (red) define your trading range and structural boundaries.
Step 3 — Wait for SMC Confluence
Before entering on a trend line touch, check whether a CHoCH has recently occurred in your direction. A CHoCH near a trend line is one of the highest-probability setups this indicator generates.
Step 4 — Watch for FVG Zones
If a Fair Value Gap is present near the trend line, this adds further confluence. Institutional traders frequently use FVG zones as entry triggers.
Step 5 — Enter on Signal
When a LONG or SHORT label appears, all five filters have aligned. Enter at the close of the signal candle. Your TP1, TP2, and SL levels will appear automatically on the chart.
Step 6 — Manage the Trade
Consider taking partial profits at TP1 (50% of position) and letting the remainder run to TP2. Move stop loss to breakeven after TP1 is hit.
Step 7 — Set Alerts
Use the 9 built-in alert conditions to be notified of signals and SMC events without watching the chart constantly.
================================================================
RECOMMENDED TIMEFRAMES
================================================================
Scalping: 1 minute, 5 minutes (Pivot Length 5-7)
Intraday: 15 minutes, 30 minutes, 1 Hour (Pivot Length 8-10)
Swing Trading: 4 Hour, Daily (Pivot Length 10-15)
Position Trading: Weekly (Pivot Length 15-20)
The indicator automatically adapts to any timeframe. Only the Pivot Lookback Length needs to be adjusted based on your trading style.
================================================================
COMPATIBLE MARKETS
================================================================
This indicator works on all liquid markets available on PulseWire:
Forex: All major, minor, and exotic currency pairs
Crypto: Bitcoin, Ethereum, and all altcoins
Commodities: Gold (XAUUSD), Silver (XAGUSD), Oil (WTI, Brent)
Indices: S&P 500, NASDAQ, Dow Jones, FTSE, DAX, Nikkei
Stocks: Any individual equity with sufficient volume
Futures: All futures contracts available on PulseWire
================================================================
WHO CAN USE THIS INDICATOR
================================================================
Beginner Traders:
The dashboard and automatic signals remove the need for manual analysis. Beginners can use the LONG and SHORT labels as guided entry points while learning how the underlying concepts work.
Intermediate Traders:
Use the BOS and CHoCH labels to understand market structure shifts and combine them with trend line touches for high-confluence entries. The FVG zones add an additional layer for precise entry timing.
Advanced and Professional Traders:
Use the full system as an institutional-grade market structure scanner. Combine BOS, CHoCH, FVG, and liquidity zones with your own HTF bias for a complete multi-confluence trading framework. The volume and RSI filters can be tuned precisely to your strategy requirements.
Algorithmic and Systematic Traders:
All 9 alert conditions can be connected to PulseWire webhooks for automated notification systems or strategy integration.
Portfolio Managers and Analysts:
The dashboard provides a rapid one-glance market assessment across any asset — useful for screening multiple instruments quickly.
================================================================
IMPORTANT DISCLAIMER
================================================================
Auto TrendLine Intelligence is a technical analysis tool designed to support trading decisions. It does not guarantee future results. All trading involves risk. Always apply proper risk management, use appropriate position sizing, and conduct your own due diligence before entering any trade. Past signal performance is not indicative of future results. This indicator is not financial advice. Indicator
Liquidity Matrix | AnonycryptousLiquidity Matrix | Anonycryptous
Description & user manual
**Credits**
The sweep detection engine in Liquidity Matrix draws conceptual inspiration from the Liquidity Sweep Filter by AlgoAlpha. The approach to identifying swing-based stop hunts, classifying sweeps by volume significance, and filtering by trend direction is based on ideas first demonstrated in their open-source script, author: AlgoAlpha (pulsewire.com/u/AlgoAlpha)
---
Why this indicator is different;
Most liquidity indicators show you one thing. A zone. A sweep marker. A supply box. A trendline. Each tool tries to solve one problem, and if you want to understand the full picture, you stack five or six indicators on the same chart until it becomes unreadable.
Liquidity Matrix works differently.
It is not a signal indicator. It does not tell you when to buy or sell. It does not score your trades or track your win rate. What it does is something more fundamental: it maps the full landscape of liquidity around price, across ten independent engines, all configurable, all in one overlay.
The core idea is that liquidity drives price. Retail traders place stops at predictable locations — below swing lows, above swing highs, at equal highs and lows, at structural pivots, inside fair value gaps. Institutional participants know this. They move price to those locations, collect that liquidity, and then move in the direction they were always going. If you understand where the liquidity is, where it has already been taken, and what levels are still sitting unmitigated, you understand the context before you place a trade.
Liquidity Matrix gives you that map.
What makes it different from other multi-engine indicators is that every engine is genuinely independent. You can run just the liquidity zones. Or just the voids and HTF levels. Or every engine at once and build your own confluence system. There is no forced reading. There is no house view on what the market is doing. You bring your methodology. The indicator gives you the context to apply it.
It also does something no single-purpose tool does: it shows you the volume behind every level. Not just where the stop clusters are — but how much liquidity was there when they formed. A zone created on 2.4M volume is not the same as a zone created on 58K. The indicator makes that difference visible.
Important notice
Liquidity Matrix does not generate trading signals.
It does not tell you when to buy or sell.
It does not predict market direction.
It does not guarantee any outcome.
All trading decisions remain entirely with the user.
Always apply your own judgment and manage your own risk.
1. Overview
Liquidity Matrix is a multi-engine liquidity context indicator built around one idea: before you place a trade, understand where the liquidity is.
What it includes:
- Liquidity zones: probability-scored pivot clusters with volume intensity rendering
- Equal highs and equal lows: zones where retail stops stack at matching price levels
- Trend engine: directional band with accumulated sweep volume tracking
- Sweep detection: swing-level stop hunt identification with safe stop placement
- HTF liquidity levels: higher timeframe high/low levels as horizontal reference lines
- Dynamic trendlines: automatically detected diagonal support and resistance with touch volume
- Supply and demand zones: SMC-based structural zones with BOS conversion
- Weekend gap: Friday close reference with gap fill tracking
- Liquidity voids: fair value gaps with gradient layer fill tracking
- RSI divergence: price chart divergence detection with candle coloring and optional trailing stop
- Dashboard: live market context across all active engines
2. Liquidity zones
2.1 How they form
Liquidity zones are identified at confirmed pivot highs and lows. A pivot forms when a price extreme holds for a configurable number of bars on both sides. Each zone is scored using a probability model that weighs four factors: distance from current price, age of the zone, whether it is still fresh (untested), and the volume present at formation.
The result is a probability score from 0 to 100 displayed on the chart. A score above 70 appears in green. Between 40 and 70 it appears in gold. Below 40 it appears in red.
The fill intensity of each zone box scales automatically with the normalized volume at formation. Higher volume at creation means a fuller, more opaque box. This makes the visual weight of each zone reflect its actual significance without requiring manual evaluation.
Zones above price are BSL — buy side liquidity. These are where long stops and breakout orders sit. Zones below price are SSL — sell side liquidity. These are where short stops and breakdown orders sit.
2.2 Sweep markers
When price breaks through the bottom of a demand zone or the top of a supply zone, a circle marker appears on the chart — above the candle for a supply zone break (bearish), below the candle for a demand zone break (bullish). The zone fades to indicate the liquidity has been consumed. This is distinct from the swing sweep detection engine, which operates independently.
2.3 Settings
Pivot left and right bars control detection sensitivity. Fewer bars on the right produces faster confirmation but reduces accuracy. The volume filter removes zones that formed on below-average activity. Fresh zones only hides tested zones to keep the chart clean. Max zones controls how many active zones are held at once. The swept zone transparency and show swept toggle control what remains visible after a zone is consumed.
2.4 In practice
Look for price approaching an unmitigated zone with a high probability score on high-volume context. The higher the score and the more opaque the box, the more likely institutional interest was present at formation. When multiple zones stack closely — visible as a cluster — that price area has concentrated stop density. When a zone is swept and the circle marker appears, the liquidity at that level has been cleared. Stops are gone. The level loses its significance as a target.
Volume note: all volume values in Liquidity Matrix are expressed in the base currency of the trading pair. On BTC/USDT, the value shown is in BTC. On SOL/USDT, it is in SOL. To convert to USD equivalent, multiply by the current price. This applies to every volume label across all engines.
3. Equal highs and equal lows
3.1 How they form
Equal highs and equal lows (EQH/EQL) are zones where price has tested the same level on two or more separate occasions without closing through it. These represent areas where retail stop orders have accumulated in a predictable way. Matching swing highs create a resistance cluster with long stops resting above. Matching swing lows create a support cluster with short stops resting below.
The zones are rendered as filled boxes using a linefill between two lines. The box spans from the lower of the two matching pivots to the higher, creating a clearly visible area of concentrated liquidity.
3.2 Settings
Tolerance (ATR×) controls how precisely two pivots must match to qualify as equal. A lower tolerance requires a near-exact match. A higher tolerance allows approximate levels to be grouped. The minimum age prevents recent, unconfirmed pivots from forming zones too early. The removal mode determines whether a zone is removed when price touches the wick, the body, or the body at twice ATR distance.
3.3 In practice
An EQH/EQL zone directly above or below price is a high-probability target for a liquidity sweep. When price approaches such a level, consider whether the move has the characteristics of a stop hunt: a spike through the level, a strong close back inside, and a reversal. The volume label on each zone shows the total volume from both matching pivots combined — giving you a sense of how much liquidity is sitting there.
4. Trend engine
4.1 How it works
The trend engine calculates a rolling volatility band using ATR. When price is above the upper band, the trend direction is bullish. When price is below the lower band, it is bearish. The band tracks the dominant directional bias and changes color accordingly.
As the trend unfolds, peak and valley levels form at local turning points within the band. The engine accumulates the volume from sweep events at each of these turning points and displays it as a label on the band. The label shows the total volume cleared at that inflection point — a direct measure of how much liquidity was consumed as the trend moved through that level.
4.2 In practice
The trend direction is shown in the dashboard as bull or bear. Use this as your macro bias filter. Look for setups in the direction of the trend. The volume labels along the band show where the significant sweeps occurred — these points represent former liquidity levels that have already been consumed and are unlikely to act as targets again. The current edge of the band is where the next sweep may occur.
5. Sweep detection
5.1 How it works
The sweep detection engine monitors rolling swing highs and lows using a configurable lookback. When price spikes through a swing level on the wick and closes back inside, a sweep event is recorded. The wick must penetrate the level by at least a minimum ATR multiple. The close must reject with a minimum strength relative to the candle range. An optional EMA filter and cooldown period reduce false triggers.
Sweeps are classified as major or minor based on normalized volume. A sweep on above-average volume is marked with a solid triangle. A below-average sweep is marked with a hollow triangle.
When a sweep fires, a safe stop line is drawn at the sweep extreme — the wick tip. This is the correct location for a stop loss after a sweep, because the liquidity that was resting there has already been consumed. Placing a stop beyond a consumed sweep is placing it where no further stop hunt is likely to occur.
5.2 Settings
The wick minimum ATR multiple and minimum rejection percentage filter out weak sweeps. The EMA filter aligns sweeps with the broader trend. The cooldown prevents repeated triggers from the same level. Major sweep threshold (normalized volume) separates significant events from minor ones.
5.3 In practice
A major sweep on a significant EQH or liquidity zone is one of the cleanest setups in the indicator. Price took the liquidity, volume confirms the institutional event, and the safe stop line gives you a clear invalidation level. The smaller the distance between current price and the safe stop line, the more attractive the risk structure.
6. HTF liquidity levels
6.1 How they form
Higher timeframe high and low levels represent the largest clusters of resting liquidity on the chart. Monthly, weekly, daily, previous day, 4-hour, and 1-hour levels are supported. Each level is drawn as a horizontal line starting at the bar time of the HTF candle that created it and extending a configurable number of bars to the right.
The volume of the HTF candle is shown as a label at the right edge of the line. Higher volume on the HTF candle means more institutional activity was present when that level formed. Monthly levels have the highest opacity. Opacity decreases progressively as timeframe decreases, so the relative significance is immediately visible.
6.2 Settings
Each timeframe is individually toggleable. Line style (solid, dashed, dotted), width, and color are configurable. Extend bars controls how far the line projects to the right. The liquidity label can be hidden if a cleaner chart is preferred.
6.3 In practice
HTF levels are major liquidity magnets. Price tends to move toward unmitigated monthly and weekly highs and lows before reversing. When a HTF level aligns with a liquidity zone or EQH/EQL cluster, the confluence strengthens the case for a sweep at that level. The dashboard shows the nearest HTF level above and below current price so you can read the closest target without examining every line on the chart.
7. Dynamic trendlines
7.1 How they form
The trendline engine scans historical pivot highs and lows and finds the best-fit diagonal line across multiple touch points. A valid trendline requires at least two confirmed touches with minimal deviation, a score that weighs touch count, recency, tightness of touches, and span. The highest-scoring line for both support and resistance is drawn automatically every bar.
Volume accumulates at each confirmed touch point. The label at the end of the line shows the total accumulated volume across all touches — the more volume that has interacted with the trendline, the more significant it is as a structural level.
A channel fill renders between the support and resistance lines and changes color based on whether price is in the upper or lower half of the channel.
7.2 Settings
Pivot length controls detection sensitivity. Lookback bars limits how far back the engine searches. ATR length controls the volatility smoothing used for touch tolerance. Max violations allows lines to remain valid after a small number of wick pierces. Touch tolerance and max distance filter noise. Extend bars projects the lines forward.
7.3 In practice
A trendline with high accumulated volume and multiple tight touches is a strong structural level. When price approaches it from inside the channel, it is approaching a level where multiple institutional interactions have occurred. The volume label tells you how much. A break and retest of such a line — particularly with a sweep marker — is a high-quality location for a trade idea.
8. Supply and demand zones
8.1 How they form
Supply and demand zones are identified using structural pivot points. When a new swing high forms, a supply zone is created at that level. When a new swing low forms, a demand zone is created. Zones are sized using an ATR-based width multiplier. An overlap filter prevents duplicate zones from stacking in the same price area.
When price breaks through a zone boundary, the zone converts to a BOS line — a thin horizontal marker showing where market structure was broken. This mirrors the SMC (smart money concepts) approach where a broken supply zone confirms bullish structure, and a broken demand zone confirms bearish structure.
8.2 Settings
Swing length controls how many bars are required on each side of a pivot for zone formation. Zone width scales the height of each box relative to ATR. The midline (POI) can be toggled to show the point of interest at the center of each zone. History to keep limits the total number of visible zones. HH/LH/HL/LL labels mark each swing point with its structural context. BOS color is configurable separately from zone colors.
8.3 In practice
Supply and demand zones show you where price left an imbalance after a structural break. When a demand zone holds and price bounces from it, the zone remains valid. When price breaks through it, the BOS line marks where that structure was invalidated. These zones work best in combination with the liquidity zone engine — when both a liquidity zone and a demand zone overlap in the same price area, the confluence raises the probability of a significant reaction.
Note on terminology: liquidity zones and supply/demand zones are different concepts. Liquidity zones mark where stop orders are likely to be resting based on pivot volume and probability scoring. Supply and demand zones mark structural imbalances where price left quickly. Both can occur at the same level, but they represent different phenomena.
9. Weekend gap
9.1 How it works
The weekend gap engine records the last confirmed Friday close price and tracks whether price returns to fill that level during the following weekend and early week. On assets that trade continuously (crypto, 24/7 markets), the gap fill tracks whether price has revisited the Friday close since the weekend began.
The Friday close is drawn as a horizontal reference line extending forward into the week. The gap fill zone renders between the Friday close and price during the relevant window. A bullish gap (price above Friday close) renders in green. A bearish gap (price below Friday close) renders in red. When the gap is filled, it clears automatically.
9.2 Settings
Line style, width, and color are configurable. Extend days controls how far the Friday close line remains visible. Bull and bear gap colors are independently adjustable.
9.3 In practice
The Friday close acts as a liquidity magnet for early week price action. Markets frequently return to fill the weekend gap before continuing in the dominant direction. The dashboard shows the gap status (open, percentage, or filled) so you can monitor it without keeping the line visible on all timeframes. On CME futures charts, the gap window is literal — the market was closed and the gap in data is visible. On crypto charts, the market was open but institutional behavior around the weekly close creates the same magnetic effect.
10. Liquidity voids
10.1 How they form
A liquidity void (also called a fair value gap or imbalance) forms when price moves quickly in one direction across three consecutive candles, leaving a gap between the wick of the first candle and the wick of the third candle. No trading occurred in that gap area. Price tends to return to fill these zones as the market seeks balance.
The void is rendered as a gradient of 13 layers. Each layer fills individually as price touches it, changing to the filled color. This gives a precise view of how much of the void has been mitigated and how much remains unvisited. A volume label shows the total volume from the bars that created the void.
10.2 Settings
Mode controls whether all historical voids are shown or only the most recent N bars. Threshold (ATR×) sets the minimum gap size relative to ATR(144) — smaller values detect more voids, larger values filter to only significant imbalances. Bullish and bearish colors are independently configurable. The filled void color can be adjusted or filled voids can be removed entirely by toggling the show filled setting.
10.3 In practice
Unfilled voids below price are areas where no transactions occurred during an upward move. They act as potential support and pullback targets. Unfilled voids above price are areas where no transactions occurred during a downward move. They act as potential resistance and rally targets. When a void aligns with a liquidity zone or HTF level, the overlap represents an area with both structural significance and a gap to fill. The volume label on each void gives you a sense of how much liquidity was consumed when the void was created — larger voids on higher volume represent more significant imbalances.
11. RSI divergence
11.1 How it works
RSI divergence occurs when price and RSI move in opposite directions at swing points. A bullish divergence forms when price makes a lower low while RSI makes a higher low — momentum is increasing even though price is still falling, which often precedes a reversal upward. A bearish divergence forms when price makes a higher high while RSI makes a lower high — momentum is weakening even though price is still rising, which often precedes a reversal downward.
The engine detects these events mechanically using pivot-based RSI analysis. When a divergence is confirmed, a box is drawn directly on the price chart spanning all candles between the two pivot points. Circle markers appear at each pivot on the price candle. This keeps the divergence signal on the chart where the price action is, rather than requiring a separate RSI pane below.
Candle coloring reflects RSI momentum continuously. When RSI is above 50 and below the overbought level, candles are colored green — the gradient becomes more intense as RSI approaches the overbought threshold. The closer RSI is to overbought, the stronger the green. When RSI crosses the overbought level, coloring stops entirely — momentum is at an extreme and the gradient no longer adds information.
The same logic applies in reverse below 50. Candles are colored red with increasing intensity as RSI approaches the oversold level. When RSI crosses below the oversold threshold, coloring stops.
This means the gradient is always telling you how much room is left in the current momentum move — fully colored means RSI is just above 50 with a long runway ahead, faded means RSI is approaching an extreme. When the color disappears, the move is at full extension.
An optional trailing stop activates after a divergence is confirmed by an RSI 50 crossover. For a bullish divergence, the stop activates when RSI crosses back above 50 and trails below price using ATR distance. It closes when price breaks the stop level or RSI reaches the overbought threshold. For a bearish divergence, the stop activates on an RSI cross below 50 and trails above price until price breaks through or RSI reaches oversold.
Note: if the RSI divergence engine is disabled while a trailing stop is active, the stop line will disappear immediately without triggering a close. Do not disable the engine mid-trade while relying on the trailing stop as an active risk tool.
11.2 Settings
RSI length — period for the RSI calculation. Default 14.
Sensitivity — controls the pivot detection window. High detects more divergences using smaller pivots. Medium is the default. Low requires larger structural pivots and produces fewer but stronger signals.
Show bullish / show bearish — each direction can be toggled independently so you only see what is relevant to your current bias.
Bullish color / bearish color — the color used for the divergence box, circle markers, candle gradient, and trailing stop line.
RSI candle coloring — toggle the gradient candle coloring on or off without affecting divergence detection.
Overbought level — RSI level at which candle coloring stops on the upside. Default 70. Raise this to 80 for assets that tend to stay overbought for extended periods.
Oversold level — RSI level at which candle coloring stops on the downside. Default 30. Raise this to 20 for assets that tend to stay oversold for extended periods.
Trailing stop — toggle the trailing stop line on or off independently.
ATR length / ATR multiplier — control the sensitivity of the trailing stop. A higher multiplier gives the stop more room and reduces premature exits on volatile assets.
11.3 In practice
Use the divergence engine as a momentum context layer on top of the liquidity engines. A bullish divergence forming at an unmitigated liquidity zone or demand zone adds significant weight to the expectation of a reversal. A bearish divergence forming just below a major HTF level or supply zone suggests the move upward may be losing momentum before reaching that target.
The dashboard row shows the current divergence state — none, bullish, or bearish — so you can monitor it without inspecting the chart.
The candle gradient is the most immediately useful visual element. Watch for candles that are deeply colored — RSI has momentum but has not yet reached an extreme. When the gradient begins fading, RSI is extending. When it disappears entirely, RSI has crossed the overbought or oversold threshold and the move is at full extension. This is often where divergence begins to form on the next cycle.
For overbought and oversold levels: on assets like BTC or ETH that can sustain strong trends, consider raising the overbought level to 75 or 80 and lowering the oversold level to 20 or 25. This prevents the coloring from stopping too early during genuine momentum moves. On more volatile altcoins where RSI whipsaws frequently around the extremes, the default 70/30 setting works well.
12. Dashboard
The dashboard displays a live summary of all active engine data in one panel. It updates every bar.
Rows shown:
Header — indicator name and timeframe label.
Trend — current direction from the trend engine: bull, bear, or ranging.
Liq zones — count of active BSL and SSL zones in view.
Nearest BSL — closest buy side liquidity level above current price.
Nearest SSL — closest sell side liquidity level below current price.
Top zone — highest probability unmitigated zone and its score.
Safe SL — current safe stop level from the sweep detection engine.
ATR (14) — current ATR value for context.
HTF — section divider for higher timeframe levels.
HTF above — nearest higher timeframe level above price.
HTF below — nearest higher timeframe level below price.
Market — section divider for market context rows.
Gap — weekend gap status: off, open (direction and percentage), or filled.
TL dist — distance from the nearest active trendline in ATR multiples.
BOS/CHoCH — whether the supply/demand structure engine is active.
EQH/EQL — count of active equal high and equal low zones.
Engines — total number of active engines.
Divergence — current RSI divergence state: none, bullish, or bearish.
Anonycryptous — version reference.
Dashboard position and text size are configurable.
13. Settings overview
Liquidity zones
- Enable/disable master toggle
- Pivot left and right bars
- Volume filter threshold
- Dynamic zone width
- Show fresh only
- Show swept zones
- Swept zone transparency
- Max zones
- Bull and bear zone colors
- Midline toggle and color
- Swept zone circle marker toggle, colors, and size
Equal highs and equal lows
- Enable/disable master toggle
- Pivot lookback length
- Tolerance (ATR×)
- Minimum age
- Removal mode (wick, body, body×2)
- EQH and EQL zone colors
- Show volume label
- Show accumulated sweep volume
Trend engine
- Enable/disable master toggle
- Trend band length
- Bull and bear band colors
- Show major/minor sweep volume labels
- Major sweep normalized volume threshold
Sweep detection
- Enable/disable master toggle
- Swing lookback
- Minimum wick ATR multiple
- Minimum rejection percentage
- EMA filter toggle, length, and timeframe
- Cooldown bars
- Major sweep normalized volume threshold
- Volume filter toggle
- Bull and bear colors
- Marker size
Safe stop line
- Line style, color, and extension bars
- Glow toggle
- Auto-remove after N bars
HTF liquidity levels
- Enable/disable master toggle
- Individual toggles for monthly, weekly, daily, previous day, 4H, P4H, 1H, P1H
- Line style, width, and color
- Extend bars
- Show liquidity label
Dynamic trendlines
- Enable/disable master toggle
- Pivot length
- Lookback bars
- ATR length
- Max violations
- Touch tolerance (ATR×)
- Max slope (degrees)
- Max distance (ATR×)
- Extend bars
- Show channel fill
- Show volume on touch
- Support and resistance colors
- Line width and style
Supply and demand zones
- Enable/disable (controls zones and BOS simultaneously)
- Swing length
- Zone width (ATR×0.1)
- History to keep
- Supply and demand colors and outlines independently
- Show midline (POI)
- Midline color
- Show HH/LH/HL/LL labels
- BOS color and line width
Weekend gap
- Enable/disable master toggle
- Show gap fill and show Friday close line independently
- Extend line (days)
- Bull and bear gap colors
- Friday close line color, width, and style
Liquidity voids
- Enable/disable master toggle
- Mode (present / historical)
- Lookback bars (for present mode)
- Void threshold (ATR×)
- Bullish and bearish void colors
- Show filled voids
- Filled void color
- Show volume label
RSI divergence
- Enable/disable master toggle
- RSI length
- Sensitivity (high, medium, low)
- Show bullish and bearish independently
- Bullish and bearish colors
- RSI candle coloring toggle
- Overbought level (default 70)
- Oversold level (default 30)
- Trailing stop toggle
- ATR length and multiplier for trailing stop
Dashboard
- Enable/disable
- Position
- Size
14. How to use
The indicator does not prescribe a method. It provides context. How you use that context depends on your own approach. The following describes the logic behind combining the engines effectively.
Start with bias. The dashboard trend row shows the current directional bias. The nearest HTF levels above and below give you the macro targets. If the weekly high is the nearest HTF above price, the market may be running toward that level before reversing.
Identify the nearest liquidity. The nearest BSL and SSL rows in the dashboard show the closest unmitigated zones. These are the most likely near-term targets for price. A high-probability zone score adds weight to the expectation that price will visit that level.
Look for confluence. When a liquidity zone, an EQH/EQL cluster, an HTF level, and a void all align at the same price area, the confluence is significant. Price has multiple reasons to move to that location. Once it arrives, multiple forms of liquidity can be consumed in one move.
Read the sweep markers. When a sweep triangle fires, liquidity was taken. The safe stop line shows the consumed level. If the sweep occurred at a high-probability zone with volume confirmation, the conditions for a reversal are in place. The triangle type (solid for major, hollow for minor) tells you how significant the volume event was.
Use the gap. In the early part of the week, the weekend gap status is visible on the dashboard. If the gap is open and price is below the Friday close, price has a tendency to return to that level. This can serve as a short-term directional bias early in the week.
Check the voids. Unfilled voids represent areas the market has not yet returned to. If price is approaching an unfilled void from outside, it is approaching a zone of imbalance that the market may seek to fill. A void aligned with a swept zone or an EQH/EQL cluster adds structural weight to the expected reaction.
15. Notes
- Liquidity Matrix is a context indicator. It maps where liquidity is, where it has been taken, and what levels remain unmitigated. It does not generate entry signals.
- All volume values are in the base currency of the trading pair. BTC/USDT shows volume in BTC. SOL/USDT shows volume in SOL. Multiply by price to approximate USD value.
- The sweep detection engine uses swing-based pivots. The supply/demand BOS engine uses a separate pivot. These are independent systems with independent lookback settings.
- On lower timeframes, more engines running simultaneously increases computation. If the indicator is slow to load, reduce the number of active engines or lower lookback values.
- HTF levels require the chart timeframe to be lower than the HTF being referenced. A daily chart will not show daily HTF levels accurately.
- The dynamic trendline engine runs every bar. On very long chart histories with tight tolerances, this may produce slightly longer load times.
- The liquidity void threshold is relative to ATR(144). On assets with low average volatility, the default threshold may produce very few voids. Reduce the threshold to increase sensitivity.
- Weekend gap tracking works on all assets. On CME futures, the gap is a literal data gap. On crypto and 24/7 assets, the gap reflects the Friday close level as an institutional reference.
16. Disclaimer
This indicator by Anonycryptous is provided for educational and informational purposes only.
All outputs are based on historical price and volume data.
Past behavior does not guarantee future results.
Trading involves substantial risk of loss.
Use at your own discretion.
Indicator
Heatmap Liquidity Zones [BigBeluga]🔵 OVERVIEW
Heatmap Liquidity Zones is a higher-timeframe volume heatmap tool designed to reveal where liquidity is concentrated inside institutional ranges.
Instead of plotting a traditional volume profile, this indicator builds a dynamic heatmap across each selected higher-timeframe candle.
It highlights high-volume price clusters, filters significant liquidity zones, and extends them forward as actionable support/resistance levels.
The result is a clean liquidity map that visualizes where participation is strongest — and where reactions are most likely to occur.
🔵 CORE CONCEPT
HTF Range Segmentation — Each higher-timeframe candle (D/W/M or custom) defines a new accumulation range.
ATR-Based Adaptive Binning — Vertical bin size is derived from ATR to maintain consistent resolution across volatility regimes.
Volume Density Mapping — Volume is distributed into price bins and normalized relative to the highest-volume bin.
Liquidity Filtering — Only bins exceeding a configurable percentage threshold are promoted to active liquidity levels.
Self-Cleaning Zones — Liquidity levels automatically disappear once breached by price.
🔵 HOW IT WORKS
1️⃣ Higher-Timeframe Reset Logic
When a new selected HTF candle begins, the previous range is finalized.
A new accumulation range starts from that bar.
High and Low are tracked dynamically throughout the segment.
2️⃣ ATR-Based Bin Construction
ATR defines the vertical bin size (ATR × Multiplier).
The total range is divided into up to Max Bins.
This ensures bin resolution adapts automatically to volatility.
3️⃣ Volume Distribution
For each completed segment, volume is distributed into bins based on proximity to bin midpoint.
Volume per bin is normalized relative to the maximum bin.
Each bin is assigned a heat color based on relative density:
Low Density → Purple
Mid Density → Cyan
High Density → Yellow
4️⃣ Liquidity Zone Creation
If a bin exceeds the Liquidity Filter %, it becomes a tracked liquidity level.
Liquidity levels extend forward as horizontal lines.
The thickness is controlled by Liquidity Level Width.
Stronger zones display larger markers and percentage labels.
🔵 HEATMAP VISUAL STRUCTURE
Completed segments display full heatmap boxes across the range.
Active segment updates in real time.
Color intensity reflects liquidity concentration.
High-density zones stand out clearly for institutional reference.
🔵 OPTIONAL MOVING AVERAGE
Optional smoothing MA overlay (SMA, EMA, RMA, WMA, VWMA).
Hidden by default.
Can be used for confluence with liquidity zones.
🔵 KEY FEATURES
Higher-timeframe segmented liquidity mapping.
ATR-based adaptive resolution.
Three-stage heatmap gradient.
Configurable liquidity filtering.
Auto-expiring support/resistance levels.
Dynamic zone thickness based on volume strength.
Real-time developing heatmap.
Optional MA overlay.
🔵 HOW TO USE
Focus on yellow (high-density) zones for strongest liquidity pools.
Watch reactions at filtered liquidity levels.
Use HTF segmentation (Weekly/Monthly) to identify institutional positioning.
Combine with breakout tools for liquidity sweep setups.
Lower ATR multiplier → more granular liquidity clusters.
Higher Liquidity Filter % → only strongest zones remain.
🔵 INTERPRETING LIQUIDITY
High density near highs → potential distribution.
High density near lows → potential accumulation.
Clustered zones → compression areas before expansion.
Thin zones → low participation, faster price movement potential.
🔵 CONCLUSION
Heatmap Liquidity Zones transforms higher-timeframe volume into a structured liquidity map.
By combining ATR-adaptive binning, density-based heat gradients, and intelligent liquidity filtering, it highlights where institutional participation is concentrated — and where meaningful reactions are most likely.
This makes it especially powerful for identifying liquidity pools, sweep zones, and structural turning points. Indicator
Liquidity Thermal Map [BigBeluga]🔵 OVERVIEW
Liquidity Thermal Map visualizes where the highest traded volume has accumulated across price levels over a fixed lookback period.
Instead of plotting classic volume profiles with bars, the indicator builds a horizontal thermal heatmap directly on the chart, highlighting areas of strong and weak liquidity using smooth color gradients.
This makes it easy to identify high-interest price zones, volume clusters, and the dominant Point of Control (PoC) at a glance.
🔵 CONCEPTS
Price-Level Volume Aggregation — The indicator divides the entire price range of the selected lookback period into fixed horizontal bins.
Volume Binning — Each bin accumulates total traded volume whenever price closes near its midpoint.
Thermal Gradient Mapping — Volume intensity is translated into a color gradient, forming a continuous liquidity heatmap.
Point of Control (PoC) — The price level with the highest accumulated volume is highlighted using a distinct PoC color.
🔵 FEATURES
Liquidity Heatmap — Displays horizontal volume concentration directly on the chart background.
Fixed Resolution Bins — Uses 30 evenly spaced price levels to maintain a clean and readable structure.
Adaptive Lookback Period — Volume is calculated only within the user-defined historical window.
Two-Stage Color Gradient —
• Low volume → transparent / muted tones
• High volume → stronger, warmer colors
PoC Highlighting — The most traded price level is emphasized with a dedicated PoC color and volume label.
Range-Aware Scaling — Automatically adapts to the highest and lowest prices within the lookback period.
🔵 BUY / SELL LIQUIDITY SCALE
Directional Liquidity Breakdown — The vertical scale on the right side summarizes how total traded volume is distributed between bullish and bearish candles within the analyzed range.
Buy Liquidity (Green) — Represents the total traded volume during candles that closed higher than they opened.
This approximates aggressive buying pressure and shows how much volume has accumulated below the current price.
Sell Liquidity (Red) — Represents the total traded volume during candles that closed lower than they opened.
This reflects periods where selling pressure dominated and shows how much volume accumulated above the current price.
Liquidity Percentage — Each side displays the percentage share of total traded volume.
This helps quickly identify which side of the market controlled the majority of activity within the lookback range.
Volume Imbalance — The Imbalance value at the top shows the absolute difference between total buy and sell liquidity.
A larger imbalance suggests stronger directional dominance from either buyers or sellers.
Interactive Hover Details — Hovering over the liquidity bars reveals a tooltip showing the exact accumulated volume for that section (for example total liquidity below the current price).
This allows traders to quickly inspect how much volume has been concentrated on each side of the market.
Visual Pressure Gauge — The vertical red/green bar acts as a quick visual gauge of market pressure, allowing traders to instantly see whether buyers or sellers dominate liquidity within the selected range.
PoC Highlighting — The most traded price level is emphasized with a dedicated PoC color and volume label.
🔵 HOW TO USE
Identify Liquidity Clusters — Bright or dense zones indicate prices where significant trading activity occurred.
Support & Resistance Context — High-volume zones often act as reaction areas for price.
PoC Tracking — The PoC shows where the market spent the most time and volume.
Breakout Awareness — Moves away from dense liquidity areas may signal expansion into lower-volume zones.
Contextual Analysis — Use the heatmap as a background liquidity reference alongside trend or structure tools.
🔵 VISUAL LOGIC
Cooler Colors — Lower volume participation.
Warmer Colors — Higher volume concentration.
PoC Label — Displays the exact volume value of the strongest liquidity level.
🔵 CONCLUSION
Liquidity Thermal Map provides a clean, intuitive way to visualize where liquidity truly exists across price.
By transforming raw volume data into a continuous thermal layer, it helps traders quickly locate dominant trading zones, identify high-interest price levels, and better understand how volume is distributed within the market.
Indicator
Stop Loss Cascades (Breakouts) [Kioseff Trading]Hello friends and traders!
🔹Introduction
This indicator " Stop-Loss Clustering (Breakouts) " attempts to model trader stop-loss placement logic and identify price areas where a large amount of stop losses might cluster.
The idea is, if stop losses are indeed highly concentrated in a specific area, price extending through that area may produce high-velocity breakout conditions via forced order flow .
I'll cover this topic more thoroughly throughout the description. For now, just know that stop loss location & size data is not publicly available . Any model of their concentration locations is highly assumptive.
However, there's some reasonable academic research we can reference to make worthwhile estimates.
Academic references supporting the concepts discussed are listed at the end of this description. To maintain readability, I won't cite individual statements inline.
🔹The Premise
🔸Liquidity, Behavior, and Stop Cascades
Markets operate through a continuous limit order book , where two fundamental order types interact:
Limit orders , which provide liquidity by resting in the book
Market orders , which consume liquidity by exhausting those resting orders
This mechanical interaction drives price movement - incoming order flow consuming available liquidity .
This begs the question.. Does liquidity distribute evenly across the LOB?
If it did : If liquidity were evenly distributed, price impact could be modeled as a relatively smooth function of incoming order flow.
But it doesn’t : Liquidity is unevenly distributed. Academic research supports this claim and, regardless, this is an intuitive conclusion most traders arrive at.
Liquidity forms localized concentrations and gaps.
Liquidity concentrations are commonly referenced as: liquidity shelves , liquidity clusters , liquidity zones .
Liquidity gaps are commonly referenced as: liquidity vacuums , thin book zones .
As a result, identical order flow can produce very different price movements depending on the state of the order book.
Let’s consider an example..
Assume price is trading at $99.
The price levels $100, $101, $102 have resting sell limit order concentrations of 100.
This is where you come in.
You execute a market order buy for 300 size.
Your order first exhausts all sell-side resting order concentrations at the $100 level.
You still have 200 size that needs to be filled, and the ask price has moved from $100 to $101.
Your order will now sequentially exhaust available liquidity at the $101 level, the ask price will increase to $102, and your final 100 size will exhaust the $102 level.
To keep the example simple, we’ll say that your order moved price from $99 to $102, and now the ask price is $103.
But, you still want to accumulate.
The nearest sell-side levels in the LOB are $103, $104, $105.
The $103 level has a sell limit order concentration of 500.
$104 and $105 both have concentrations of 50.
You execute your same market order buy for 300 size.
This time, price doesn’t move.. At all..
Instead, you consumed 300 of the 500 size at $103 with your order, and the level remains a barrier.
Your order was absorbed by available liquidity.
This example demonstrates how price movement depends on available liquidity , not simply the size of incoming orders.
In the first scenario, liquidity was thin and the order walked through multiple price levels, causing price to move quickly.
In the second scenario, a large concentration of resting liquidity absorbed the same order, preventing price from advancing.
🔸Liquidity Does Not Distribute Evenly
Alright, we understand that liquidity doesn’t distribute evenly. And we understand that high concentrations of liquidity can act as price barriers (liquidity shelves) while sparse liquidity can permit rapid price movement - we saw this in our example above.
There’s an important question we should ask next before we move on..
If liquidity distributes unevenly, then where does it tend to cluster? And where does it tend to thin?
Of course, knowing these tendencies provides multi-purpose advantages.
If price approaches a liquidity vacuum - a local block of the order book with thin resting liquidity - rapid price movement can occur without requiring unusually strong aggressive order flow.
If price approaches a liquidity shelf - a local block of the order book with thick resting liquidity - price can stall or contract even if the same level of aggressive order flow that previously moved price continues.
With this in mind, order flow intensity alone does not determine price movement . The distribution of liquidity across surrounding price levels plays a similarly important role.
So, is there any evidence of where liquidity tends to concentrate ?
🔸Empirical Observations
Empirical research on limit order books shows that liquidity does not distribute smoothly across the LOB . Instead, depth tends to concentrate at specific price levels, producing irregular profiles with localized peaks in resting liquidity.
These concentrations arise because order placement is not random . Traders frequently anchor decisions to widely observed reference prices such as:
• prior highs
• prior lows
• round numbers
• widely referenced price extremes
Because many traders monitor the same price history, order placement decisions often reference similar price levels.
This concept is simpler than it sounds.
Let’s use market structure traders for example.
Market structure traders frequently reference prior swing highs and swing lows when making decisions about entries, exits, and risk.
A trader entering a long position may place their stop-loss below a recent swing low , reasoning that if price breaks that level, the trade idea is invalidated.
A trader entering a short position may place their stop-loss above a recent swing high for the same reason.
Timeframe price aggregation may differ; however, we’re all looking at roughly the same recent highs and lows when evaluating a chart (structure).
When many traders collectively reference the same prices, orders may accumulate near those levels. This produces localized depth concentrations, which traders refer to as liquidity shelves .
Liquidity shelves act as temporary barriers where the book contains disproportionately large resting liquidity compared to surrounding prices.
🔸Research documenting liquidity clustering includes :
Bourghelle & Cellier (2007) , who find that limit orders cluster at prominent price levels (especially round numbers), creating localized depth concentrations that can act as price barriers.
Kavajecz & Odders-White (2004) , who demonstrate that prices identified as support or resistance coincide with higher resting limit order depth
These findings suggest that many commonly observed price levels may correspond to real concentrations of liquidity rather than being purely visual artifacts on a chart.
Kavajecz & Odders-White (2004) is an important observation for support/resistance traders!
Kavajecz & Odders-White (2004) show that levels traders commonly call support and resistance often align with areas where more limit orders are resting in the order book.
This suggests a plausible mechanical pathway through which support and resistance levels can emerge!
🔸Liquidity Shelves and Price Interaction
When liquidity clusters around a price level, the resulting liquidity shelf can influence how price behaves when it approaches that area.
Price interaction with these shelves is state-dependent :
If incoming order flow is absorbed, price may stall or reverse
If resting liquidity is consumed, price may transition rapidly to the next liquidity zone
Once a shelf is depleted, follow-through can accelerate due to thinner liquidity beyond the level
Research on order book dynamics supports this mechanical view of price movement.
For example:
Jean-Philippe Bouchaud, J. Doyne Farmer, and Fabrizio Lillo (2009) demonstrate that price impact emerges from the interaction between order flow and finite liquidity
From this perspective, price does not move simply because a level is crossed.
Price moves because available liquidity at that level has been consumed.
🔸Latent Liquidity and Stop Clustering
In addition to visible liquidity from limit orders, markets also contain latent liquidity .
This is where ”Stop-Loss Clustering (Breakouts)” becomes important - we’re almost done!
Latent liquidity consists of conditional orders such as stop-losses that are not visible in the order book until triggered .
Although these orders aren’t public information, empirical studies show that stop orders tend to cluster near widely referenced price levels .
Research by Carol Osler (2001, 2002) using institutional FX order data finds that stop-loss orders frequently accumulate just beyond salient price levels such as prior highs and lows.
When these stops trigger, they convert into aggressive market orders and can generate bursts of directional order flow that may accelerate price movement.
🔸Stop-Loss Cascades
Stop losses add another layer of latent order flow that isn’t visible in the order book until it triggers.
If enough of them sit around the same price area.. Think “hidden pressure” waiting to activate. Nothing happens while price trades nearby, but once that level is traded at, those stops convert into market orders and immediately begin consuming available liquidity.
This matters because stop placement is unlikely to be random in most instances. Traders frequently anchor stops to widely observed prices such as prior highs, prior lows, or other prominent structure points, or use volatility methods such as ATR, etc.
So when price approaches one of these areas, two things can happen.
If the resting liquidity there is large enough, the incoming orders can be absorbed and price may stall or reject.
But if that liquidity gets consumed, the stops sitting just beyond the level begin triggering. Those triggered stops add additional market orders, which consume more liquidity and can push price further into the next layer of stops.
This creates a cascading effect:
price reaches a stop cluster
stops trigger and convert into market orders
liquidity gets consumed faster
price moves further, triggering more stops
When this chain reaction starts, price can transition very quickly from a slow battle near the level to rapid expansion through it.
This is one of the mechanical reasons why some reference-point breaks barely move, while others accelerate rapidly.
🔹How It Works
Now that we understand the why - let’s discuss how the indicator works.
🔸Absorbtion Extremes
The image above shows the absorption extremes model.
In this model, the indicator treats recent & relevant swing points as plausible stop clustering candidates.
You can find similar swing point identification mechanics in other indicators.
However, this model assigns subsequent volume to the swing level after its formation.
There are limitations and assumptions - let’s go over them.
The images above explain how the indicator determines the intensity of a possible stop-cluster around a swing level.
There are limitations and assumptions
1: The indicator assigns all “directional volume” to a swing level after it’s formed and while it remains the closest active swing point to the current price.
“Buy volume” is assigned to the closest active swing low.
“Sell volume” is assigned to the closest active swing high.
I say “buy volume” and “sell volume” because there’s assumptions on what constitutes the relevant classification.
The indicators follow the traditional two-region tick model for classifying buy volume and sell volume.
Higher close = “buy volume” proxy
Lower close = “sell volume” proxy
Depending on the granularity you select (the indicator is capable of using tick data), this model can be more/less accurate.
However, even with tick-level data and bid/ask quotes, trade direction must still be inferred using classification rules. Because some trades occur inside the spread or involve hidden liquidity, perfect classification is not possible without exchange aggressor flags.
For assumptions..
The model assigns ALL classified volume to the swing level.
In reality, traders use a wide range of risk management methods, and not every position will place a stop loss directly at the most recent swing point. ATR-based stops, percentage-based stops, and other volatility-based methods are also common.
Because the true distribution of stop placement is unobservable, the model assumes that positions entered are structurally invalidated at the closest swing level based on their classified direction.
As a result, the values displayed by the indicator should be interpreted as relative proxies for potential stop concentration, rather than precise estimates of actual stop-loss size.
The displayed magnitudes are intentionally exaggerated and comparative, designed to highlight where stop pressure may accumulate relative to other levels.
The images above show how to interpret the indicator when using this model.
The image above shows the triggered stop-cluster graph.
Each point corresponds to a triggered stop-cluster - assuming it exists.
The greater the size attached to that cluster, the further distant the data point is placed.
Far away from zero line = large size.
Close to zero line = low size.
Radiating/glowing points indicate a potentially large cluster trigger.
🔸 Volatility-At-Entry Model (Time Scaled)
The Volatility-At-Entry model uses ATR scaled by various timeframes to predict plausible stop loss placements.
For this model, the indicator uses the same tick classification model to assign volume directionally.
Volume is then dispersed across six common timeframes (1m, 5m, 15m, 30m, 1h, 4h) and 3 common ATR multiples for risk management (1ATR, 1.5ATR, 2ATR).
This model assumes traders are entering positions across various timeframes and are scaling risk congruent with those timeframes.
For instance,
A trader using the 1-minute chart for opportunity is more likely to use a stop loss closer to entry than a trader using the 4-hour chart for opportunity.
If this assumption is reasonable to you - great, we can move forward!
The image above visualizes the model.
Purple-shaded regions indicate a price area with less opportunity for stop loss clustering. Either transaction intensity around eligible price areas was low, or position accumulation wasn’t given sufficient time.
Pink-shaded regions indicate a price area with greater opportunity for stop loss clustering. Volume was significant around these regions or price has traded within proximity for extended periods.
This model naturally shows more future opportunity than historical outcomes. You can select to show historical outcomes in the settings, this image shows examples of such outcomes.
The image above shows the triggered stop loss graph in effect for this model. Stop clustered are distributed across more price areas with this model - from low intensity to high intensity. Therefore, a cluster is almost always “triggering” to some degree.
A classification model for what’s typical and what’s unusual is used for the graph in this case. Radiating points always indicate large stop clusters triggered. Anything within the green/pink line indicates usual size.
Typical Move
The image above explains the nearest cluster information table.
The size and location of the nearest buy-stop cluster and sell-stop cluster are recorded.
Additionally, the indicator identifies whether clusters of similar size were triggered in the past, and how price behaved following those events.
Since all models here are highly assumptive, and similar sized clusters might only have one or two relative neighbors, treat these measurements as a description of history rather than a prediction.
The model takes the logarithm of the current stop-volume (buy or sell) to normalize its scale and compare it with a historical dataset of previously observed stop-volume sizes that have also been log-scaled.
It then identifies historical observations whose sizes are most similar to the current value, either by selecting all observations within a tolerance range around that value (where the range is based on the typical spacing between historical observations), or by selecting the single closest match.
Finally, the model retrieves the historical price moves associated with those matched observations, producing a sample of “typical moves” that occurred when stop-volume magnitude was similar to the current situation.
Ratio Meter
The stop-cluster ratio meter shows the current sum of active and triggered all buy-side clusters and sell-side clusters.
This meter is useful for quick scanning across assets to see if active or recently triggered stop clusters are lopsided.
Additional Features
The single most important setting outside model selection is the lower timeframe used to retrieve volume from.
This setting is set to 1-minute data by default because it works with paid and free plans. If you want better granularity, I strongly suggest changing this setting to either 1-second or 1-tick. This will sacrifice the number of identifiable cluster locations, because better granularity data has less programmatically retrievable values.
🔹Closing Remarks
Stop-loss clustering is an appealing concept because it offers a plausible explanation for why some breakouts accelerate so quickly while others stall. When a large number of conditional orders sit near the same price, a breakout through that area can trigger a cascade of market orders that rapidly consume liquidity and push price toward the next available zone.
However, it’s important to remember that the models used in this indicator are approximations, not direct measurements. True stop-loss locations and sizes are not publicly observable, and many traders use different risk management techniques that cannot be perfectly inferred from chart data alone. The goal of this indicator is therefore not to identify exact stop locations, but to highlight price areas where stop pressure may plausibly accumulate relative to surrounding levels.
Like any model based on behavioral assumptions and historical observations, results should be interpreted probabilistically. Large clusters do not guarantee breakouts, and small clusters do not guarantee quiet price behavior. Instead, the indicator is best used as a tool for context and situational awareness.
References
General Microstructure and Price Formation
Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205–258.
O'Hara, M. (1995). Market Microstructure Theory. Blackwell.
Biais, B., Glosten, L., & Spatt, C. (2005). Market microstructure: A survey of microfoundations, empirical results, and policy implications. Journal of Financial Markets, 8(2), 217–264.
Limit Order Books and Liquidity as Resting Orders
Gould, M. D., Porter, M. A., Williams, S., McDonald, M., Fenn, D. J., & Howison, S. D. (2013). Limit order books. Quantitative Finance, 13(11), 1709–1742.
Rosu, I. (2009). A dynamic model of the limit order book. Review of Financial Studies, 22(11), 4601–4641.
Biais, B., Hillion, P., & Spatt, C. (1995). An empirical analysis of the limit order book and the order flow in the Paris Bourse. Journal of Finance, 50(5), 1655–1689.
Liquidity Clustering and Depth Concentration
Kavajecz, K. A., & Odders-White, E. R. (2004). Technical analysis and liquidity provision. Review of Financial Studies, 17(4), 1043–1071.
Bourghelle, D., & Cellier, A. (2007). Limit order clustering and price barriers on financial markets. Working paper / SSRN.
Order Flow and Price Impact
Bouchaud, J.-P., Farmer, J. D., & Lillo, F. (2009). How markets slowly digest changes in supply and demand. In Handbook of Financial Markets: Dynamics and Evolution.
Stop Orders and Price Cascades
Osler, C. L. (2003). Currency orders and exchange-rate dynamics: Explaining the success of technical analysis. Journal of Finance, 58(5), 1791–1819.
Osler, C. L. (2005). Stop-loss orders and price cascades in currency markets. Journal of International Money and Finance, 24(2), 219–241.
Liquidity Provision and Execution
Ho, T., & Stoll, H. (1981). Optimal dealer pricing under transactions and return uncertainty. Journal of Financial Economics, 9(1), 47–73.
Almgren, R., & Chriss, N. (2000). Optimal execution of portfolio transactions. Journal of Risk, 3(2), 5–39.
Menkveld, A. J. (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712–740.
Behavioral Anchoring and Attention
Kahneman, D., & Tversky, A. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785–818.
George, T. J., & Hwang, C. Y. (2004). The 52-week high and momentum investing. Journal of Finance, 59(5), 2145–2176.
Mizrach, B., & Weerts, S. (2007). Highs and lows: A behavioral and technical analysis. SSRN working paper.
Indicator
Liquidity Pools + Sweep Signals [Metrify]If breakouts feel like a scam, it’s because they often function like one.
Most charts are taught like they’re a clean story of supply and demand. But real price action is messier: it’s a sequence of tests, traps, and collections. The market doesn’t need to “respect” your line, it needs to find liquidity.
And liquidity usually sits in predictable places: swing highs, swing lows, prior reaction points, the levels everyone can see.
This Liquidity Sweep Canvas is a market-structure overlay that tracks liquidity pools built from swing highs/lows, then monitors how price interacts with those pools over time (touches → sweeps → breaks/expiry). The goal is not to “predict” — it’s to map where liquidity is parked, highlight when it’s raided with rejection, and keep a clean, visual “canvas” of relevant pools near current market.
It builds two sides:
SELL liquidity pools (from pivot highs, shown in red)
BUY liquidity pools (from pivot lows, shown in teal)
Each pool is zoned around the pooled level, merges nearby levels (optional aggressiveness), tracks hits, and can transition through states:
Active (building / being respected)
Swept (liquidity taken + rejection confirmed)
Ended (broken through or expired)
Sweep logic in plain terms
A sweep is detected when price pierces beyond a pool boundary and then closes back through the pool’s midline in the opposite direction (rejection).
Bear sweep (SELL liquidity): price wicks above a SELL pool, then closes back below the pool mid.
Bull sweep (BUY liquidity): price wicks below a BUY pool, then closes back above the pool mid.
Optionally, you can require a second-step confirmation:
Displacement confirm waits for follow-through (within a small window) where price breaks beyond the sweep candle’s reference (with a minimum body size in ATR). This filters some noise, at the cost of being delayed.
🔥 Scoring system (how “quality” is decided)
Sweeps are common. Clean sweeps are not. We uses a weighted scoring model (0–100) so you can filter out weak sweeps and keep the ones that show stronger intent.
A sweep starts when price penetrates beyond the pool boundary (takes liquidity) and reclaims back inside the zone (closes through the pool mid). From there, a score is built from two layers:
✅ Layer 1 —> Sweep candle “core bundle” (base part)
This is computed immediately on the sweep candle (or stored if you require displacement). The base bundle blends:
Penetration: how deep the wick pushed beyond the pool in ATR terms (not “deeper is always better”, it’s shaped to reward a realistic sweet spot).
Reclaim strength: how much of the candle reclaimed back (close relative to the range).
Wick ratio: rejection wick size vs body (controlled by 'Wick Ratio Scale').
Body bias: bullish body for bull sweeps / bearish body for bear sweeps gets rewarded.
EMA context: measures whether the sweep is happening with a favorable distance relative to EMA 200.
Line age/maturity: longer pools can score differently via a length score, then get penalized by a separate age penalty.
🧠 Layer 2 —> Context add-ons
After the base bundle, the final score can include:
MSS context: a simple structural reference (recent swing extreme lookback) to rate whether the sweep is happening with useful positioning.
Effort score: combines range expansion (ATR) with volume vs volume MA to reward sweeps that show actual participation.
Displacement score (optional): if enabled, the sweep is only confirmed after follow-through within a small window.
How to use it
1. Build a two-stage decision: location bias, then trigger selection
Use pools to decide directional bias before you even consider entries. If price is pressing into SELL pools repeatedly and the dashboard shows dense sell-side activity, your bias shifts toward expecting a sell-side raid (sweep up then rejection) rather than a clean breakout. If price is pressing into BUY pools, same logic for downside raid and bounce. Then decide your trigger style manually:
If you trade fast mean reversion, you can use immediate sweeps as the “first alarm” and enter on the reclaim + tight invalidation.
If you trade safer confirmation, require displacement confirm, and only act once price has proven it can leave the pool with force.
Either way, the script helps you separate where it matters (pools) from where it doesn’t (middle of nowhere).
2. Use hit count to judge liquidity density and trap probability
The LP xN hit count is a manual edge if you treat it correctly: more hits generally implies more eyes, more orders, more liquidity, and therefore more potential for a meaningful raid. When you see a pool with high hits near current price, don’t assume it’s “strong support/resistance.” Instead, assume it’s a liquidity magnet.
If price repeatedly taps a high-hit pool without breaking cleanly, it often sets up a sweep (stop run + reverse).
If price breaks and stays outside with follow-through, that’s not a sweep environment, it’s a continuation environment.
So you use hit count to anticipate which levels are likely to be hunted, then use candle behavior + displacement to judge whether the hunt was successful and rejected.
3. Turn sweeps into ‘event markers’ for post-move structure mapping
Instead of treating a sweep as “enter now,” treat it as: a structural event happened here.
After a sweep prints, manually re-map microstructure: identify the last minor swing before the sweep, then track whether price breaks it (MSS/BOS style) and whether the first pullback respects that break.
4. Use the channel read as a regime filter (premium/discount logic)
The nearest pool edges effectively form a liquidity channel. Use it like a regime filter:
Inside SELL zone / premium: prioritize short-side narratives
Inside BUY zone / discount: prioritize long-side narratives
Middle channel: treat as uncertainty, tighten your standards (or step aside).
5. Use scoring as a ‘quality gate’, then you do the narrative check”
If you enable scoring, stop thinking of it as “higher score = higher win.” Think of it as a gate that filters out low-effort pokes. Once a high-score sweep prints, manually audit it.
6. Use it as a ‘sweep journal’ to study your market’s behavior
A very “pro” use is not trading it at all for a week. Turn on historical traces and sweep markers, and just observe: Which sessions produce the cleanest sweeps? Do high-score sweeps outperform low-score? Do confirmed sweeps reduce chop at the cost of late entries? Does your instrument sweep more on highs or lows? The dashboard counts help you quantify frequency. After you collect observations, you tune inputs (Swing Length, Merge Distance, Minimum Score, Volume thresholds) to match the instrument’s microstructure.
This is how you turn a generic sweep concept into a market-specific playbook—and the script becomes your data-driven visual log, not a guessing machine.
⚙️ Tuning tips (fast)
Too many pools / too noisy → increase Swing Length / Merge Distance.
Sweeps trigger too often → enable Activate Scoring and raise Min Score.
Wick quality not valued enough → reduce Wick Ratio Scale.
Effort scoring feels too easy/hard → adjust Min Volume / MA and Volume MA Length.
A higher score is not a guarantee of a better trade, it simply means the sweep event matched more of the model’s criteria (penetration, reclaim, rejection wick, effort, context components, and optional displacement). Markets are adaptive: what high quality looks like changes by instrument, timeframe, and session. Use scoring to reduce noise, then manually validate. Indicator
Smart Money Structure | GainzAlgo📊 OVERVIEW:
================
Smart Money Structure Analysis is a professional-grade market structure and order-flow system designed to identify institutional trading behavior through volatility-adaptive logic, multi-timeframe trend alignment, and volume-based confirmation.
This indicator implements original mathematical models to detect Change of Character (CHoCH), Break of Structure (BOS), cumulative volume dynamics, and trend convergence across seven timeframes — delivering high-probability trade signals with significantly reduced noise.
Unlike basic indicator combinations, this system functions as a unified trading framework, where volatility adaptation, structure analysis, and volume confirmation continuously reinforce each other to provide precise, context-aware signals.
⭐ WHY THIS SYSTEM IS UNIQUE AND WORTHY OF PUBLICATION:
=====================================================
This is not a collection of common indicators placed together.
Smart Money Structure Analysis represents a cohesive institutional methodology, engineered so that:
- Volatility adjusts signal sensitivity in real time
- Multi-timeframe trends define directional bias
- Market structure determines timing
- Volume confirms institutional participation
- Advanced filters eliminate low-quality setups
Each component is mathematically linked to the others, creating a workflow that cannot be replicated by stacking separate indicators.
🔗 SYNERGISTIC INTEGRATION – HOW THE SYSTEM WORKS TOGETHER:
==========================================================
🧠 1. CONTEXT-AWARE VOLATILITY ADAPTATION
ATR-based volatility logic dynamically adjusts all momentum thresholds:
- Higher volatility → stronger confirmation required
- Lower volatility → sensitivity increases to capture valid moves
This prevents over-signaling in choppy markets and under-signaling during expansion phases — a core flaw in static indicators.
📐 2. MULTI-TIMEFRAME TREND CONVERGENCE ENGINE
Seven timeframes are analyzed simultaneously:
1M • 5M • 15M • 30M • 1H • 4H • 1D
Each timeframe is scored using EMA + VWAP alignment, producing a composite Trend Strength Score from -100 to +100.
The stronger the alignment across timeframes, the higher the probability of continuation — instantly visible through the real-time dashboard.
🏗️ 3. INSTITUTIONAL MARKET STRUCTURE (CHoCH & BOS)
The system automatically identifies the two core smart money concepts:
- CHoCH (Change of Character):
Signals potential trend exhaustion or reversal zones
- BOS (Break of Structure):
Confirms trend continuation and institutional commitment
Structure zones are visualized with persistent, color-coded levels and clouds, providing precise contextual timing rather than lagging signals.
📊 4. CUMULATIVE VOLUME DELTA (CVD) CONFIRMATION
CVD tracks the cumulative difference between buying and selling pressure:
- Rising CVD → accumulation
- Falling CVD → distribution
- Divergence vs price → early reversal warning
Volume participation is categorized into Low / Medium / High, adding depth beyond simple volume bars.
🛡️ 5. SIX-LAYER PROFESSIONAL SIGNAL FILTERING
Every signal must pass through up to six independent confirmation layers:
1. Volatility-adjusted momentum
2. Higher timeframe trend alignment
3. Lower timeframe conflict prevention
4. Institutional volume confirmation
5. Structural breakout validation
6. Repeated-signal restriction
This dramatically reduces false positives while preserving only high-quality institutional setups.
🧮 DETAILED CORE SYSTEMS:
========================
📏 ADAPTIVE MOMENTUM FORMULA
- Momentum Threshold = Base × (1 + (ATR ÷ Price) × 2)
- Pre-Momentum Factor = Base × (1 − (ATR ÷ Price) × 0.5)
📊 TREND STRENGTH CALCULATION
- Trend Strength = (Sum of 7 timeframe scores ÷ 7) × 100
📦 CVD LOGIC
- Close > Previous Close → Buy volume added
- Close < Previous Close → Sell volume subtracted
- Cumulative sum reveals institutional intent
🧠 STRUCTURE DETECTION
- Pivot-based swing logic
- Candle confirmation
- Configurable lookback periods
- Non-repainting visualization
🧩 ADVANCED ANALYSIS TOOLS:
==========================
🧲 LIQUIDITY ZONE DETECTION
Identifies probable retail stop-loss clusters where institutions often initiate stop hunts before true directional moves.
📦 MARKET PROFILE & ORDER FLOW IMBALANCE
Detects buy/sell dominance using volume ratios, highlighting accumulation and distribution zones before large price moves.
🔄 RSI DIVERGENCE SCANNER
Identifies bullish and bearish divergences that frequently precede structure shifts and trend reversals.
🎨 VISUAL SYSTEM & DASHBOARD:
============================
📊 SMART MONEY MATRIX
- Composite trend strength
- System confidence %
- CVD value
- Directional grid for all timeframes
📈 TREND PREDICTION MATRIX (Optional)
Forecasts short-term directional bias using trend, momentum, and volatility data.
🏷️ SIGNAL LABELS
- BUY / SELL → Fully confirmed entries
- READY → Momentum building
- BOS / CHoCH → Structure events
- FLOW / LIQ / BULL / BEAR → Advanced confirmations
⚙️ CORE FEATURES:
================
1. Multi-Timeframe Trend Convergence
2. Smart Money Structure Detection (CHoCH & BOS)
3. Adaptive Volatility-Based Momentum
4. Cumulative Volume Delta (CVD)
5. Six-Layer Signal Filtering
6. Liquidity Zone Detection
7. Order Flow & Market Profile Analysis
8. Divergence Scanner
9. Dynamic Trendlines
10. Institutional-Grade Dashboard
📘 WHO THIS INDICATOR IS FOR:
============================
- Scalpers: Noise-filtered precision on lower timeframes
- Day Traders: High-probability continuation setups
- Swing Traders: Multi-timeframe alignment & structure zones
- Reversal Traders: Divergence + CHoCH confirmation
⚠️ IMPORTANT DISCLAIMER:
========================
This indicator is a technical analysis and educational tool only.
It does not provide financial advice or trade recommendations.
Trading involves substantial risk, and losses are a natural part of trading.
Past performance does not guarantee future results.
All trading decisions remain the sole responsibility of the user. Indicator
Support and ResistanceSupport & Resistance Zones
This indicator automatically identifies support and resistance zones by clustering confirmed pivot highs and lows into statistically valid price areas.
Instead of drawing single horizontal lines, it creates price zones whose width is dynamically controlled using ATR (Average True Range), allowing the zones to adapt to market volatility.
Core Logic
The indicator scans a user-defined number of historical bars and detects pivot highs and pivot lows using a configurable pivot strength.
Each new pivot is evaluated against previously detected zones:
A zone becomes visible only after receiving sufficient confirmation (minimum number of pivot touches).
This ensures that only structurally meaningful levels are drawn.
Zone Construction Rules
Zones are formed by grouping pivot points whose total price range remains within ATR range
Each zone expands dynamically as new pivots confirm it
Zones are drawn as rectangular areas, not lines
Zones extend to the right, remaining active until price structure changes
This approach avoids over-plotting and reduces noise commonly seen in traditional support/resistance tools.
Dynamic Zone Coloring
Zones automatically change color based on current price position:
Support Color → Price is above the zone
Resistance Color → Price is below the zone
Neutral (In-Zone) Color → Price is trading inside the zone
This makes it easy to visually assess market context without additional indicators.
Inputs Explained
Logic Settings
Bars to Apply
Number of historical bars scanned to detect pivots and construct zones.
Pivot Strength
Number of candles required on both sides of a pivot high/low for confirmation.
Min Pivot Confirmation
Minimum number of aligned pivots required before a zone is drawn.
Styling
Support, resistance, and in-zone colors
Zone fill transparency
Why This Approach
Uses price structure, not arbitrary levels
Adapts to market volatility via ATR
Filters out weak, single-touch levels
Works across all markets and timeframes
This indicator is designed to highlight areas of interest, not generate buy or sell signals.
It is best used in combination with trend, momentum, or volume-based tools. Indicator
Amihud Illiquidity Ratio [MarkitTick]💡This indicator implements the Amihud Illiquidity Ratio, a financial metric designed to measure the price impact of trading volume. It assesses the relationship between absolute price returns and the volume required to generate that return, providing traders with insight into the "stress" levels of the market liquidity.
Concept and Originality
Standard volume indicators often look at volume in isolation. This script differentiates itself by contextualizing volume against price movement. It answers the question: "How much did the price move per unit of volume?" Furthermore, unlike static indicators, this implementation utilizes dynamic percentile zones (Linear Interpolation) to adapt to the changing volatility profile of the specific asset you are viewing.
Methodology
The calculation proceeds in three distinct steps:
1. Daily Return: The script calculates the absolute percentage change of the closing price relative to the previous close.
2. Raw Ratio: The absolute return is divided by the volume. I have introduced a standard scaling factor (1,000,000) to the calculation. This resolves the issue of the values being astronomically small (displayed as roughly 0) without altering the fundamental logic of the Amihud ratio (Absolute Return / Volume).
- High Ratio: Indicates that price is moving significantly on low volume (Illiquid/Thin Order Book).
- Low Ratio: Indicates that price requires massive volume to move (Liquid/Deep Order Book).
3. Dynamic Regimes: The script calculates the 75th and 25th percentiles of the ratio over a lookback period. This creates adaptive bands that define "High Stress" and "Liquid" zones relative to recent history.
How to Use
Traders can use this tool to identify market fragility:
- High Stress Zone (Red Background): When the indicator crosses above the 75th percentile, the market is in a High Illiquidity Regime. Price is slipping easily. This is often observed during panic selling or volatile tops where the order book is thin.
- Liquid Zone (Green Background): When the indicator drops below the 25th percentile, the market is in a Liquid Regime. The market is absorbing volume well, which is often characteristic of stable trends or accumulation phases.
- Dashboard: A visual table on the chart displays the current Amihud Ratio and the active Market Regime (High Stress, Normal, or Liquid).
Inputs
- Calculation Period: The lookback length for the average illiquidity (Default: 20).
- Smoothing Period: The length of the additional moving average to smooth out noise (Default: 5).
- Show Quant Dashboard: Toggles the visibility of the on-screen information table.
● How to read this chart
• Spike in Illiquidity (Red Zones)
Price is moving on "thin air." Expect high volatility or potential reversals.
• Low Illiquidity (Green/Stable Zones)
The market is deep and liquid. Trends here are more sustainable and reliable.
• Divergence
Watch for price making new highs while liquidity is drying up—a classic sign of an exhausted trend.
Example:
● Chart Overview
The chart displays the Amihud Illiquidity indicator applied to a Gold (XAUUSD) 4-hour timeframe.
Top Pane: Price action with manual text annotations highlighting market reversals relative to liquidity zones.
Bottom Pane: The specific technical indicator defined in the logic. It features a Blue Line (Raw Illiquidity), a Red Line (Signal/Smoothed), and dynamic background coloring (Red and Green vertical strips).
● Deep Visual Analysis
• High Stress Regime (Red Zones)
Visual Event: In the bottom pane, the background periodically shifts to a translucent red.
Technical Logic: This event is triggered when the amihudAvg (the smoothed illiquidity ratio) exceeds the 75th percentile ( hZone ) of the lookback period.
Forensic Interpretation: The logic calculates the absolute price change relative to volume. A spike into the red zone indicates that price is moving significantly on relatively lower volume (high price impact). Visually, the chart shows these red zones aligning with local price peaks (volatility expansion), leading to the bearish reversal marked by the red box in the top pane.
• Liquid Regime (Green Zones)
Visual Event: The background shifts to a translucent green in the bottom pane.
Technical Logic: This triggers when the amihudAvg falls below the 25th percentile ( lZone ).
Forensic Interpretation: This state represents a period where large volumes are absorbed with minimal price impact (efficiency). On the chart, this green zone corresponds to the consolidation trough (green box, top pane), validating the annotated accumulation phase before the bullish breakout.
• Indicator Lines
Blue Line: This is the illiquidityRaw value. It represents the raw daily return divided by volume.
Red Line: This is the smoothedVal , a Simple Moving Average (SMA) of the raw data, used to filter out noise and define the trend of liquidity stress.
● Anomalies & Critical Data
• The Reversal Pivot
The transition from the "High Stress" (Red) background to the "Liquid" (Green) background serves as a visual proxy for market regime change. The chart shows that as the Red zones dissipate (volatility contraction), the market enters a Green zone (efficient liquidity), which acted as the precursor to the sustained upward trend on the right side of the chart.
● About Yakov Amihud
Yakov Amihud is a leading researcher in market liquidity and asset pricing.
• Brief Background
Professor of Finance, affiliated with New York University (NYU).
Specializes in market microstructure, liquidity, and quantitative finance.
His work has had a major impact on both academic research and practical investment models.
● The Amihud (2002) Paper
In 2002, he published his influential paper: “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects” .
• Key Contributions
Introduced the Amihud Illiquidity Measure, a simple yet powerful proxy for market liquidity.
Demonstrated that less liquid stocks tend to earn higher expected returns as compensation for liquidity risk.
The measure became one of the most widely used liquidity metrics in finance research.
● Why It Matters in Practice
Used in quantitative trading models.
Applied in portfolio construction and risk management.
Helpful as a liquidity filter to avoid assets with excessive price impact.
In short: Yakov Amihud established a practical and robust link between liquidity and returns, making his 2002 work a cornerstone in modern financial economics.
Disclaimer: All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicator
Simple Line📌 Understanding the Basic Concept
The trend reverses only when the price moves up or down by a fixed filter size.
It ignores normal volatility and noise, recognizing a trend change only when price moves beyond a specified threshold.
Trend direction is visually intuitive through line colors (green: uptrend, red: downtrend).
⚙️ Explanation of Settings
Auto Brick Size: Automatically determines the brick/filter size.
Fixed Brick Size: Manually set the size (e.g., 15, 30, 50, 100, etc.).
Volatility Length: The lookback period used for calculations (default: 14).
📈 Example of Identifying Buy Timing
When the line changes from gray or red to green, it signals the start of an uptrend.
This indicates that the price has moved upward by more than the required threshold.
📉 Example of Identifying Sell Timing
When the line changes from green to red, it suggests a possible downtrend reversal.
At this point, consider closing long positions or evaluating short entries.
🧪 Recommended Use Cases
Use as a trend filter to enhance the accuracy of existing strategies.
Can be used alone as a clean directional indicator without complex oscillators.
Works synergistically with trend-following strategies, breakout strategies, and more.
🔒 Notes & Cautions
More suitable for medium- to long-term trend trading than for fast scalping.
If the brick size is too small, the indicator may react to noise.
Sensitivity varies greatly depending on the selected brick size, so backtesting is essential to determine optimal values.
❗ The Trend Simple Line focuses solely on direction—remove the noise and focus purely on the trend.
초대 전용 스크립트
이 스크립트에 대한 접근이 제한되어 있습니다. 사용자는 즐겨찾기에 추가할 수 있지만 사용하려면 사용자의 권한이 필요합니다. 연락처 정보를 포함하여 액세스 요청에 대한 명확한 지침을 제공해 주세요.
이 비공개 초대 전용 스크립트는 스크립트 모더레이터의 검토를 거치지 않았으며, 하우스 룰 준수 여부는 확인되지 않았습니다. 트레이딩뷰는 스크립트의 작동 방식을 충분히 이해하고 작성자를 완전히 신뢰하지 않는 이상, 해당 스크립트에 비용을 지불하거나 사용하는 것을 권장하지 않습니다. 커뮤니티 스크립트에서 무료 오픈소스 대안을 찾아보실 수도 있습니다.
작성자 지시 사항
.
c9indicator
면책사항
해당 정보와 게시물은 금융, 투자, 트레이딩 또는 기타 유형의 조언이나 권장 사항으로 간주되지 않으며, 트레이딩뷰에서 제공하거나 보증하는 것이 아닙니 Indicator
Dynamic Liquidity HeatMap Profile [BigBeluga]🔵 OVERVIEW
The Dynamic Liquidity HeatMap Profile is a smart-flow liquidity tracker that maps where stop-loss clusters and resting limit orders are likely positioned.
Instead of traditional volume profiles based only on executed transactions, this tool projects probable liquidity pools — areas where traders are trapped or positioned and where smart money may hunt stops or fill orders.
It dynamically scans recent price swings, builds liquidity zones above and below price, and visualizes them as a heat map + histogram — highlighting areas with the greatest liquidity attraction.
Orange highlights the highest-concentration liquidity (POC), making potential sweep targets obvious.
🔵 CONCEPTS
Liquidity pools form above swing highs (buy stops) and below swing lows (sell stops).
Market makers & large players often push price into these zones to trigger stops and capture liquidity.
The indicator uses recent volatility + volume expansion to estimate where these pools exist.
Horizontal heat bars show depth and intensity of probable liquidity.
Profile side histogram displays buy-side vs sell-side liquidity distribution.
🔵 FEATURES
Dynamic Liquidity Detection — finds potential stop-loss clusters from recent swing behavior.
Dual-Side Heatmap — split liquidity view above (short stops) and below (long stops) current price.
Volume-Weighted Levels — higher volatility & volume = deeper liquidity expectation.
Real-Time Heat Coloring
• Lime = liquidity below price (potential buy-side fuel)
• Blue = liquidity above price (potential sell-side fuel)
• Orange = peak liquidity (POC)
Liquidity Profile Histogram — plotted at right side, layered by strength.
Auto-Cleaning Engine — removes invalidated liquidity after breaks.
Adjustable lookback window and bin resolution .
🔵 HOW TO USE
Look for price moving toward dense liquidity zones — high probability of wick raids or sweeps.
Orange POC often acts as magnet — strong target zone for smart money.
Combine with SFP / BOS logic to time reversals after liquidity hunts.
In trend, price repeatedly sweeps opposite-side liquidity before continuation.
Use liquidity walls as bias filters — heavy liquidity above often precedes downward move, and vice-versa.
Great for scalping sessions, indices, FX, BTC, ETH.
🔵 CONCLUSION
The Dynamic Liquidity HeatMap Profile gives traders a tactical edge by revealing where the market’s hidden liquidity resides.
It highlights where shorts and longs are positioned, identifies likely sweep zones, and marks the most attractive liquidity magnet (POC).
Use it to anticipate stop hunts, avoid getting trapped, and align with smart-money flow instead of fighting it.
Indicator
Smarter Money Concepts - OBs [PhenLabs]📊 Smarter Money Concepts - OBs
Version: PineScript™ v6
📌 Description
Smarter Money Concepts - OBs (Order Blocks) is an advanced technical analysis tool designed to identify and visualize institutional order zones on your charts. Order blocks represent significant areas of liquidity where smart money has entered positions before major moves. By tracking these zones, traders can anticipate potential reversals, continuations, and key reaction points in price action.
This indicator incorporates volume filtering technology to identify only the most significant order blocks, eliminating low-quality signals and focusing on areas where institutional participation is likely present. The combination of price structure analysis and volume confirmation provides traders with high-probability zones that may attract future price action for tests, rejections, or breakouts.
🚀 Points of Innovation
Volume-Filtered Block Detection : Identifies only order blocks formed with significant volume, focusing on areas with institutional participation
Advanced Break of Structure Logic : Uses sophisticated price action analysis to detect legitimate market structure breaks preceding order blocks
Dynamic Block Management : Intelligently tracks, extends, and removes order blocks based on price interaction and time-based expiration
Structure Recognition System : Employs technical analysis algorithms to find significant swing points for accurate order block identification
Dual Directional Tracking : Simultaneously monitors both bullish and bearish order blocks for comprehensive market structure analysis
🔧 Core Components
Order Block Detection : Identifies institutional entry zones by analyzing price action before significant breaks of structure, capturing where smart money has likely positioned before moves.
Volume Filtering Algorithm : Calculates relative volume compared to a moving average to qualify only order blocks formed with significant market participation, eliminating noise.
Structure Break Recognition : Uses price action analysis to detect legitimate breaks of market structure, ensuring order blocks are identified only at significant market turning points.
Dynamic Block Management : Continuously monitors price interaction with existing blocks, extending, maintaining, or removing them based on current market behavior.
🔥 Key Features
Volume-Based Filtering : Filter out insignificant blocks by requiring a minimum volume threshold, focusing only on zones with likely institutional activity
Visual Block Highlighting : Color-coded boxes clearly mark bullish and bearish order blocks with customizable appearance
Flexible Mitigation Options : Choose between “Wick” or “Close” methods for determining when a block has been tested or mitigated
Scan Range Adjustment : Customize how far back the indicator looks for structure points to adapt to different market conditions and timeframes
Break Source Selection : Configure which price component (close, open, high, low) is used to determine structure breaks for precise block identification
🎨 Visualization
Bullish Order Blocks : Blue-colored rectangles highlighting zones where bullish institutional orders were likely placed before upward moves, representing potential support areas.
Bearish Order Blocks : Red-colored rectangles highlighting zones where bearish institutional orders were likely placed before downward moves, representing potential resistance areas.
Block Extension : Order blocks extend to the right of the chart, providing clear visualization of these significant zones as price continues to develop.
📖 Usage Guidelines
Order Block Settings
Scan Range : Default: 25. Defines how many bars the indicator scans to determine significant structure points for order block identification.
Bull Break Price Source : Default: Close. Determines which price component is used to detect bullish breaks of structure.
Bear Break Price Source : Default: Close. Determines which price component is used to detect bearish breaks of structure.
Visual Settings
Bullish Blocks Color : Default: Blue with 85% transparency. Controls the appearance of bullish order blocks.
Bearish Blocks Color : Default: Red with 85% transparency. Controls the appearance of bearish order blocks.
General Options
Block Mitigation Method : Default: Wick, Options: Wick, Close. Determines how block mitigation is calculated - “Wick” uses high/low values while “Close” uses close values for more conservative mitigation criteria.
Remove Filled Blocks : Default: Disabled. When enabled, order blocks are removed once they’ve been mitigated by price action.
Volume Filter
Volume Filter Enabled : Default: Enabled. When activated, only shows order blocks formed with significant volume relative to recent average.
Volume SMA Period : Default: 15, Range: 1-50. Number of periods used to calculate the average volume baseline.
Min. Volume Ratio : Default: 1.5, Range: 0.5-10.0. Minimum volume ratio compared to average required to display an order block; higher values filter out more blocks.
✅ Best Use Cases
Identifying high-probability support and resistance zones for trade entries and exits
Finding optimal stop-loss placement behind significant order blocks
Detecting potential reversal areas where price may react after extended moves
Confirming breakout trades when price clears major order blocks
Building a comprehensive market structure map for medium to long-term trading decisions
Pinpointing areas where smart money may have positioned before major market moves
⚠️ Limitations
Most effective on higher timeframes (1H and above) where institutional activity is more clearly defined
Can generate multiple signals in choppy market conditions, requiring additional filtering
Volume filtering relies on accurate volume data, which may be less reliable for some securities
Recent market structure changes may invalidate older order blocks not yet automatically removed
Block identification is based on historical price action and may not predict future behavior with certainty
💡 What Makes This Unique
Volume Intelligence : Unlike basic order block indicators, this script incorporates volume analysis to identify only the most significant institutional zones, focusing on quality over quantity.
Structural Precision : Uses sophisticated break of structure algorithms to identify true market turning points, going beyond simple price pattern recognition.
Dynamic Block Management : Implements automatic block tracking, extension, and cleanup to maintain a clean and relevant chart display without manual intervention.
Institutional Focus : Designed specifically to highlight areas where smart money has likely positioned, helping retail traders align with institutional perspectives rather than retail noise.
🔬 How It Works
1. Structure Identification Process :
The indicator continuously scans price action to identify significant swing points and structure levels within the specified range, establishing a foundation for order block recognition.
2. Break Detection :
When price breaks an established structure level (crossing below a significant low for bearish breaks or above a significant high for bullish breaks), the indicator marks this as a potential zone for order block formation.
3. Volume Qualification :
For each potential order block, the algorithm calculates the relative volume compared to the configured period average. Only blocks formed with volume exceeding the minimum ratio threshold are displayed.
4. Block Creation and Management :
Valid order blocks are created, tracked, and managed as price continues to develop. Blocks extend to the right of the chart until they are either mitigated by price action or expire after the designated timeframe.
5. Continuous Monitoring :
The indicator constantly evaluates price interaction with existing blocks, determining when blocks have been tested, mitigated, or invalidated, and updates the visual representation accordingly.
💡 Note:
Order Blocks represent areas where institutional traders have likely established positions and may defend these zones during future price visits. For optimal results, use this indicator in conjunction with other confluent factors such as key support/resistance levels, trendlines, or additional confirmation indicators. The most reliable signals typically occur on higher timeframes where institutional activity is most prominent. Start with the default settings and adjust parameters gradually to match your specific trading instrument and style. Indicator
