EdgeXplorer - Liquidity ScopeLiquidity Scope by EdgeXplorer
Liquidity Scope is a real-time liquidity detection system developed for traders who want to track where the market is hunting stops, absorbing orders, and setting up traps — often before the average eye catches on. Built to identify the telltale behavior of liquidity sweeps and false breakouts, this tool highlights areas on the chart where price interacts with key swing points, including wicks, breaks, and retests.
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🔍 What Does Liquidity Scope Do?
Liquidity Scope scans price action for swing highs and lows, tracks how price behaves around them, and visually plots zones where liquidity is likely being targeted. It tells you:
• When price wicks into a previous swing without breaking it (a liquidity probe),
• When price breaks past that level and returns (a potential retest),
• And when a sweep is complete or mitigated.
The result? A visual map of where liquidity was grabbed, where it hasn’t been yet, and where price might revisit — all drawn directly on your chart, in real time.
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⚙️ How It Works – Technical Breakdown
Here’s the logic behind the engine:
1. Swing Detection
The script uses ta.pivothigh() and ta.pivotlow() to mark structural swing points, using your selected “Swings” length to define sensitivity.
2. Sweep Conditions
For each swing high or low:
• If price wicks into the level but fails to close beyond it → potential liquidity test.
• If price closes beyond the swing → it’s marked as broken.
• If price later retests the broken level from the other side → it’s tagged as a retest zone.
3. Visual Memory
Each swing level stores its own “memory state” (whether it was wicked, broken, retested, or mitigated), allowing the tool to update visuals live and avoid clutter.
4. Dynamic Zones
• When a sweep is detected, the tool draws a colored zone (box) at the sweep location, along with a supporting line.
• These zones extend forward until price clearly invalidates or mitigates them.
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📈 Visual Components – What You See on the Chart
Element Meaning
Green Zones / Lines Bullish sweep: liquidity hunted below a swing low
Red Zones / Lines Bearish sweep: liquidity hunted above a swing high
Dotted Lines Wicks — price tested the level without breaking
Dashed Lines Retests — price returned to retest a broken level
Solid Lines Confirmed sweep levels with clean structure
Shaded Boxes Sweep zones extended into the future for monitoring
Faded Transparency Indicates mitigation or that the zone is cooling off
Every visual is tied to a logic branch in the code — nothing is decorative. Each shape or line has meaning tied to price behavior.
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📊 Inputs & Settings Explained
Setting Description
Swings (len) Sets the pivot lookback range. Higher = fewer, stronger swing levels.
Options (opt) Controls what sweep types you want to see:
• Only Wicks → Focus on traps and fakeouts
• Only Outbreaks & Retest → Focus on confirmed moves
• Wicks + Outbreaks & Retest → See it all |
| Bull/Bear Colors | Customize how bullish vs. bearish sweeps are drawn |
| Extend Zones (extend) | When on, boxes stretch forward in time until price touches or invalidates them |
| Max Bars (maxB) | Sets how long (in bars) sweep zones will stay active before expiring |
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🧠 How to Read It in Live Markets
Liquidity Scope doesn’t tell you what to do — it tells you what the market just did in relation to liquidity and structure.
Here’s how to use it:
• Green Zones (Bullish Sweeps):
Price just grabbed liquidity under a low. Watch for:
• A bounce → potential reversal
• A retest → possible long entry confirmation
• Red Zones (Bearish Sweeps):
Price swept above a high. Watch for:
• Immediate rejection → potential short zone
• Pullback and retest → trend continuation trap or fake breakout
• Wick Sweeps Only:
Often seen in range-bound markets or when market makers are testing stops.
• Retest Sweeps:
Often seen in trending markets, validating breakouts or signaling exhaustion.
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🧪 Optional Use Cases & Strategy Tips
Here’s how traders on the EdgeXplorer platform use Liquidity Scope:
• 🔄 Smart Money Concepts: Use sweep zones alongside order blocks, FVGs, and breakers to confirm institutional movement.
• ⚠️ Trap Zones: Spot liquidity fakeouts where retail might be chasing early breakouts.
• 🎯 Entry/Exit Filtering: Use zones to validate entries only when price reacts cleanly around them — or exits when mitigation completes.
• 🧠 Confluence Layer: Combine with trend indicators or volume to add strength to directional bias.
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🔒 Final Note on Use & Compliance
Liquidity Scope is a market behavior visualizer, not a signal generator. It helps you understand where the market might be trapping liquidity, but you are the strategy. Always pair with proper confirmation, risk management, and your own discretion.
All logic, structure, and assets in this script are © protected under ETAPX Inc. and the EdgeXplorer platform. Unauthorized sharing or monetization of this code is prohibited under company and platform policy.
ETAPX
EdgeXplorer - Gaussian Forecast GridEdgeXplorer – Gaussian Forecast Grid
The Gaussian Forecast Grid is a forward-looking market modeling tool that uses a Gaussian Process Regression framework to estimate future price behavior. Built around a non-parametric machine learning approach, it maps recent historical price data to generate smoothed forecasts, offering an evolving yet mathematically grounded projection of where price could be headed.
This is not a “signal generator”—it’s a probabilistic estimation tool that overlays a fitted baseline with a future-facing forecast curve, giving traders visual guidance on short-term trend expectations while accounting for noise and variance in price behavior.
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🔍 What Does the Gaussian Forecast Grid Do?
Gaussian Forecast Grid takes a fixed historical training sample of price data and fits it using a Gaussian kernel, generating two key visual elements:
• Fit Line — a smoothed, mathematically reconstructed version of the past data window
• Forecast Line — a forward-projected estimation of price behavior based on the shape and curvature of the past data
Traders can adjust how sensitive the model is to local volatility, how smooth the prediction curve is, and how frequently the forecast updates.
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⚙️ How It Works – Technical Logic Explained
1. Kernel Regression Foundation
The tool applies a Gaussian kernel function that evaluates similarity between time steps in a defined window. This results in a covariance matrix that models how likely different values are to move together.
kernel(x1, x2) = exp( - (x1 - x2)² / (2 * scale²) )
• X-axis: Time steps
• Y-axis: Price deviations from baseline
• Scale: Smoothing factor (determines how tight or loose the fit is)
2. Training Phase
A fixed number of bars (Data Sample Length) are selected as the training window, from which the tool:
• Computes a baseline average (via SMA)
• Normalizes price deviations
• Builds a covariance matrix for training (with optional noise)
• Inverts the matrix to solve for weights
3. Forecast Generation
With the model trained:
• Future time steps (Projection Steps) are mapped
• The kernel is applied between past and future points
• A projected set of values is generated based on how past structure likely evolves
4. Model Refresh Options
Users can control when the model retrains:
• Lock Forecast: Generates forecast once and holds it
• Update Once Reached: Recomputes after reaching the end of the forecast window
• Continuously Update: Recalculates forecast on every new bar
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📈 What Each Visual Element Represents
Visual Component Meaning
Blue Line (Fit) A smoothed curve fitted to historical price behavior
Red Line (Forecast) Projected price path based on Gaussian inference
Baseline The mean price used to normalize the data
Polyline Split Left = historical fit, Right = projected future
These lines are dynamically drawn and cleared based on model refresh mode, ensuring only relevant and current data is displayed.
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📊 Inputs & Settings Explained
Training Inputs
Setting Description
Data Sample Length How many bars are used to fit the model (higher = smoother, slower)
Fit Color Color for the historical fit curve
Forecast Controls
Setting Description
Projection Steps Number of future bars to forecast
Prediction Color Color of the projected forecast line
Model Behavior
Setting Description
Smoothing Factor Controls the “tightness” of the curve; lower values = more reactive
Noise Scale Adds Gaussian noise to prevent overfitting; useful in high-volatility assets
Model Behavior (Refresh Mode)
• Lock Forecast = static output
• Update Once Reached = refresh after forecast ends
• Continuously Update = live update every bar
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🧠 How to Interpret It in Real Markets
This indicator does not tell you where price is going. Instead, it provides a smoothed probabilistic path based on the recent shape of price movement.
Use Cases:
• 🧭 Price Projection Framing: Align other tools (like OBs, liquidity zones, or support/resistance) within the estimated trajectory
• 🔄 Reversion vs. Continuation: Compare current price position relative to the forecast path to judge whether the market is returning to structure or breaking from it
• 📐 Bias Context: Use forecast slope direction to determine short-term directional bias
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🧪 Strategy Integration Tips
• Pair with a volatility filter to use only when price is ranging or compressing
• Overlay with SMC tools like OB, FVG, or BOS indicators for confirmation
• Use as a visual narrative tool to avoid chasing price blindly during uncertain phases
