Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
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[boitl] Trendfilter🧭 Trend Filter – Curve View (1D / 1H + M15 Check)
A multi-timeframe trend filter that blends daily, hourly, and 15-minute data into a smooth, color-coded curve displayed in a separate panel.
It visualizes both trend direction and strength while accounting for overextension, providing a reliable “context indicator” for entries and filters.
🔍 Concept
The indicator evaluates three timeframes:
1D (Daily) → SMA200 for long-term trend bias
1H (Hourly) → EMA50 for medium-term confirmation
15M (Intraday) → EMA20 + ATR to detect overextension or mean reversion zones
It computes a continuous trend score between −1 and +1:
+1 → Strong bullish alignment (D1 & H1 both up)
−1 → Strong bearish alignment (D1 & H1 both down)
≈ 0 → Neutral, conflicting, or overextended conditions
The score is smoothed and normalized for a clean visual curve —
green for bullish, red for bearish, with dynamic transparency based on strength.
⚙️ Logic Overview
Timeframe Indicator Purpose
1D SMA200 Long-term trend direction
1H EMA50 Medium-term confirmation
15M EMA20 + ATR Overextension control
Alignment between D1 and H1 defines clear trend bias
Conflicts between them reduce the trend score
M15 overextension (price far from EMA20) softens the signal further
The result is a responsive trend-strength oscillator, ideal for multi-timeframe setups.
🧩 Use Cases
As a trend filter for strategies (e.g. allow entries only if score > 0.3 or < −0.3)
As a visual confirmation of higher-timeframe direction
To avoid trades during conflict or exhaustion
💡 Visualization
Single curve (area plot):
Green = bullish bias
Red = bearish bias
Transparency increases with weaker trend
Background colors:
🟠 Orange → D1/H1 conflict
🔴 Light red → M15 overextension active
Optional: binary alignment line (+1 / 0 / −1) for simplified display
⚙️ Parameters
Proximity to EMA20 (M15) = X×ATR → defines “near” condition
Overextension threshold = X×ATR → sets exhaustion boundary
EMA smoothing → reduces noise for a smoother score
Toggle overextension impact on/off
AlphaFlow - Trend DetectorOVERVIEW
AlphaFlow identifies and tracks large volume moves by combining volume analysis, price impact measurement, and conviction scoring to separate significant institutional moves from normal trading activity. Rather than just flagging high volume, this indicator evaluates whether large trades actually moved the market and assigns conviction levels based on multiple confirmation factors.
WHAT MAKES THIS ORIGINAL
This is not simply a volume indicator or volume-weighted price tracker. The originality lies in the multi-factor conviction scoring system that evaluates whether large volume moves represent genuine institutional conviction or just noise.
Key Differentiators:
- Combines volume ratio AND price impact (volume alone doesn't mean conviction)
- Conviction scoring system that weighs trend alignment, follow-through, and volume persistence
- Cumulative flow tracking that shows persistent directional pressure over time
- Market regime detection (bullish/bearish/sideways) based on flow dynamics
- Tiered signal system (EXTREME/HIGH/MEDIUM conviction) rather than binary signals
This approach solves the problem of volume spikes that don't lead to meaningful price action, or price moves on low volume that don't persist.
HOW IT WORKS
1. Whale Detection Engine:
Volume Qualification: Compares current volume to a rolling average (default 50 bars). Whale activity requires volume to be at least 1.5x the average (adjustable).
Price Impact Requirement: Volume alone isn't enough. The bar must also show significant price movement (default 0.1% minimum). This filters out high-volume consolidation where no one is actually committed to direction.
Direction Identification: Bullish whale = close > open on high volume. Bearish whale = close < open on high volume.
2. Conviction Scoring System:
The indicator doesn't just flag whale activity - it evaluates conviction through multiple factors:
Base Conviction: Calculated from (volume_ratio × price_impact) / 10
This gives higher scores to moves with both exceptional volume AND large price swings.
Trend Alignment Bonus (1.5x multiplier): Whale moves aligned with the 20-period EMA trend receive higher conviction scores. Institutional money tends to accumulate with the trend, not against it.
Follow-Through Bonus (1.3x multiplier): After whale activity, does price continue in that direction over the next bars (default 3)? Genuine conviction shows persistence.
Volume Persistence (1.2x multiplier): Is elevated volume sustained over multiple bars, or is it a one-time spike? The 3-bar average volume ratio above 1.5x indicates sustained interest.
Conviction Levels:
- EXTREME: Score > 15 (large whale emoji labels, highest confidence)
- HIGH: Score > 8 (triangle signals, strong confidence)
- MEDIUM: Score > 3 (small triangles, moderate confidence)
- LOW: Score < 3 (not plotted to reduce noise)
3. Cumulative Flow Analysis:
Rather than treating each whale move in isolation, the indicator tracks cumulative flow using an EMA of whale activity. This reveals persistent directional pressure.
Flow Calculation: Each whale bar contributes (whale_strength × direction) to the flow. Strength is volume_ratio × price_impact_percent.
Flow Momentum: Rate of change in the cumulative flow (5-bar change)
Flow Acceleration: Second derivative (3-bar change of momentum)
These metrics reveal whether whale activity is accelerating, decelerating, or reversing.
4. Market Regime Detection:
Bullish Regime: Cumulative flow > 2 AND momentum positive
Bearish Regime: Cumulative flow < -2 AND momentum negative
Sideways Regime: Neither condition met
The background color reflects the current regime, helping traders understand the broader context.
5. Flow Strength Meter:
The main plot normalizes cumulative flow to a -100 to +100 scale based on the 100-bar range. This provides a consistent visual reference regardless of the asset or timeframe.
Extreme levels at ±50 indicate particularly strong directional flow where reversals or consolidation become more likely.
HOW TO USE IT
Settings Configuration:
Whale Detection Section:
- Volume Average Period (default 50): Shorter periods make detection more sensitive to recent volume changes. Longer periods require more exceptional volume to trigger.
- Whale Volume Multiplier (default 1.5): How much above average volume must be to qualify. Lower = more signals. Higher = only extreme moves.
- Minimum Price Impact (default 0.1%): Filters out high-volume bars that didn't actually move price. Adjust based on asset volatility.
Trend Analysis:
- Trend Strength Period (default 20): EMA period for trend alignment bonus
- Confirmation Bars (default 3): How many bars to check for follow-through
Visual Settings:
- Flow Strength Meter: Main plot showing normalized cumulative flow
- Conviction Labels: Detailed labels showing volume ratio and price impact on extreme/high conviction whales
- Trend Background: Color-coded regime indication
Signal Interpretation:
EXTREME Conviction (Whale Emoji Labels):
These are the highest confidence signals. Large volume with significant price impact, aligned with trend, showing follow-through. These often mark the beginning or continuation of strong moves.
HIGH Conviction (Large Triangles):
Strong signals meeting most criteria. Good for main entries or adding to positions.
MEDIUM Conviction (Small Triangles):
Whale activity present but with fewer confirmation factors. Use for partial positions or require additional confirmation.
Flow Strength Meter:
- Above zero and rising: Bullish flow building
- Below zero and falling: Bearish flow building
- Approaching ±50: Extreme readings, watch for exhaustion
- Crossing zero: Flow regime change
Dashboard Information:
The top-right table shows:
- Current regime (bullish/bearish/sideways)
- Flow strength value
- Last whale direction
- Conviction level of last whale
- Current volume ratio
- Flow momentum direction
- Indicator status
Trading Strategies:
Trend Following: Take EXTREME and HIGH conviction signals aligned with the flow meter direction. Enter when flow is positive and rising for bullish whales, negative and falling for bearish whales.
Regime-Based: Only trade in bullish/bearish regimes (colored backgrounds). Avoid trading in sideways regimes where whale moves tend to reverse quickly.
Flow Reversals: When flow meter crosses zero with EXTREME conviction whale in the new direction, this often marks regime changes.
Exhaustion Plays: When flow reaches ±50 extreme levels, watch for EXTREME conviction whales in the opposite direction as potential reversal signals.
TECHNICAL DETAILS
Volume Ratio = Current Volume / SMA(Volume, Period)
Price Impact % = ABS(Close - Open) / Open × 100
Whale Detected = (Volume Ratio >= Multiplier) AND (Price Impact >= Minimum)
Whale Direction = Close > Open ? 1 : -1
Base Conviction = (Volume Ratio × Price Impact %) / 10
Trend Alignment = Whale Direction == Trend Direction ? 1.5 : 1.0
Follow-Through = Price continues whale direction over N bars ? 1.3 : 1.0
Volume Persistence = SMA(Volume Ratio, 3) > 1.5 ? 1.2 : 1.0
Final Conviction = Base × Trend Alignment × Follow-Through × Volume Persistence
Whale Flow = Whale Detected ? (Volume Ratio × Price Impact × Direction) : 0
Cumulative Flow = EMA(Whale Flow, 20)
Flow Momentum = Change(Cumulative Flow, 5)
Flow Acceleration = Change(Momentum, 3)
Normalized Flow Strength = (Cumulative Flow / Highest(ABS(Cumulative Flow), 100)) × 100
WHAT THIS SOLVES
Common Volume Indicator Problems:
- Volume spikes that don't move price (consolidation noise)
- Price moves on low volume that quickly reverse
- No differentiation between strong and weak volume signals
- Treating all high-volume bars equally regardless of context
- No measure of whether volume represents conviction or panic
Whale Flow Solutions:
- Requires both volume AND price impact for signals
- Conviction scoring separates strong moves from weak ones
- Cumulative flow shows persistent pressure vs isolated spikes
- Trend alignment and follow-through filter low-quality signals
- Tiered system lets traders choose their confidence threshold
LIMITATIONS
- Cannot identify individual whales or attribute volume to specific entities
- High volume can come from many sources (whales, retail panic, algo activity)
- Works best on liquid assets with consistent volume patterns
- Less reliable on low-volume assets or during market closures
- Conviction scoring thresholds may need adjustment per asset/timeframe
- Does not predict future whale activity, only identifies it after bars close
- Flow can remain at extremes longer than expected during strong trends
- False signals can occur during news events or earnings
- Not a standalone trading system - requires risk management and other analysis
Best used in combination with price action, support/resistance, and broader market context.
EDUCATIONAL VALUE
For traders learning about:
- Volume analysis beyond simple volume indicators
- Multi-factor signal confirmation systems
- Market regime and flow concepts
- Conviction-based scoring methodologies
- Cumulative indicator design
- Normalized plotting for cross-asset comparison
- Pine Script table and dashboard creation
Not financial advice.
Simplified Percentile ClusteringSimplified Percentile Clustering (SPC) is a clustering system for trend regime analysis.
Instead of relying on heavy iterative algorithms such as k-means, SPC takes a deterministic approach: it uses percentiles and running averages to form cluster centers directly from the data, producing smooth, interpretable market state segmentation that updates live with every bar.
Most clustering algorithms are designed for offline datasets, they require recomputation, multiple iterations, and fixed sample sizes.
SPC borrows from both statistical normalization and distance-based clustering theory , but simplifies them. Percentiles ensure that cluster centers are resistant to outliers , while the running mean provides a stable mid-point reference.
Unlike iterative methods, SPC’s centers evolve smoothly with time, ideal for charts that must update in real time without sudden reclassification noise.
SPC provides a simple yet powerful clustering heuristic that:
Runs continuously in a charting environment,
Remains interpretable and reproducible,
And allows traders to see how close the current market state is to transitioning between regimes.
Clustering by Percentiles
Traditional clustering methods find centers through iteration. SPC defines them deterministically using three simple statistics within a moving window:
Lower percentile (p_low) → captures the lower basin of feature values.
Upper percentile (p_high) → captures the upper basin.
Mean (mid) → represents the central tendency.
From these, SPC computes stable “centers”:
// K = 2 → two regimes (e.g., bullish / bearish)
=
// K = 3 → adds a neutral zone
=
These centers move gradually with the market, forming live regime boundaries without ever needing convergence steps.
Two clusters capture directional bias; three clusters add a neutral ‘range’ state.
Multi-Feature Fusion
While SPC can cluster a single feature such as RSI, CCI, Fisher Transform, DMI, Z-Score, or the price-to-MA ratio (MAR), its real strength lies in feature fusion. Each feature adds a unique lens to the clustering system. By toggling features on or off, traders can test how each dimension contributes to the regime structure.
In “Clusters” mode, SPC measures how far the current bar is from each cluster center across all enabled features, averages these distances, and assigns the bar to the nearest combined center. This effectively creates a multi-dimensional regime map , where each feature contributes equally to defining the overall market state.
The fusion distance is computed as:
dist := (rsi_d * on_off(use_rsi) + cci_d * on_off(use_cci) + fis_d * on_off(use_fis) + dmi_d * on_off(use_dmi) + zsc_d * on_off(use_zsc) + mar_d * on_off(use_mar)) / (on_off(use_rsi) + on_off(use_cci) + on_off(use_fis) + on_off(use_dmi) + on_off(use_zsc) + on_off(use_mar))
Because each feature can be standardized (Z-Score), the distances remain comparable across different scales.
Fusion mode combines multiple standardized features into a single smooth regime signal.
Visualizing Proximity - The Transition Gradient
Most indicators show binary or discrete conditions (e.g., bullish/bearish). SPC goes further, it quantifies how close the current value is to flipping into the next cluster.
It measures the distances to the two nearest cluster centers and interpolates between them:
rel_pos = min_dist / (min_dist + second_min_dist)
real_clust = cluster_val + (second_val - cluster_val) * rel_pos
This real_clust output forms a continuous line that moves smoothly between clusters:
Near 0.0 → firmly within the current regime
Around 0.5 → balanced between clusters (transition zone)
Near 1.0 → about to flip into the next regime
Smooth interpolation reveals when the market is close to a regime change.
How to Tune the Parameters
SPC includes intuitive parameters to adapt sensitivity and stability:
K Clusters (2–3): Defines the number of regimes. K = 2 for trend/range distinction, K = 3 for trend/neutral transitions.
Lookback: Determines the number of past bars used for percentile and mean calculations. Higher = smoother, more stable clusters. Lower = faster reaction to new trends.
Lower / Upper Percentiles: Define what counts as “low” and “high” states. Adjust to widen or tighten cluster ranges.
Shorter lookbacks react quickly to shifts; longer lookbacks smooth the clusters.
Visual Interpretation
In “Clusters” mode, SPC plots:
A colored histogram for each cluster (red, orange, green depending on K)
Horizontal guide lines separating cluster levels
Smooth proximity transitions between states
Each bar’s color also changes based on its assigned cluster, allowing quick recognition of when the market transitions between regimes.
Cluster bands visualize regime structure and transitions at a glance.
Practical Applications
Identify market regimes (bullish, neutral, bearish) in real time
Detect early transition phases before a trend flip occurs
Fuse multiple indicators into a single consistent signal
Engineer interpretable features for machine-learning research
Build adaptive filters or hybrid signals based on cluster proximity
Final Notes
Simplified Percentile Clustering (SPC) provides a balance between mathematical rigor and visual intuition. It replaces complex iterative algorithms with a clear, deterministic logic that any trader can understand, and yet retains the multidimensional insight of a fusion-based clustering system.
Use SPC to study how different indicators align, how regimes evolve, and how transitions emerge in real time. It’s not about predicting; it’s about seeing the structure of the market unfold.
Disclaimer
This indicator is intended for educational and analytical use.
It does not generate buy or sell signals.
Historical regime transitions are not indicative of future performance.
Always validate insights with independent analysis before making trading decisions.
Momentum-Based Fair Value Gaps [BackQuant]Momentum-Based Fair Value Gaps
A precision tool that detects Fair Value Gaps and color-codes each zone by momentum, so you can quickly tell which imbalances matter, which are likely to fill, and which may power continuation.
What is a Fair Value Gap
A Fair Value Gap is a 3-candle price imbalance that forms when the middle candle expands fast enough that it leaves a void between candle 1 and candle 3.
Bullish FVG : low > high . This marks a bullish imbalance left beneath price.
Bearish FVG : high < low . This marks a bearish imbalance left above price.
These zones often act as magnets for mean reversion or as fuel for trend continuation when price respects the gap boundary and runs.
Why add momentum
Not all gaps are equal. This script measures momentum with RSI on your chosen source and paints each FVG with a momentum heatmap. Strong-momentum gaps are more likely to hold or propel continuation. Weak-momentum gaps are more likely to fill.
Core Features
Auto FVG Detection with size filters in percent of price.
Momentum Heatmap per gap using RSI with smoothing. Multiple palettes: Gradient, Discrete, Simple, and scientific schemes like Viridis, Plasma, Inferno, Magma, Cividis, Turbo, Jet, plus Red-Green and Blue-White-Red.
Bull and Bear Modes with independent toggles.
Extend Until Filled : keep drawing live to the right until price fully fills the gap.
Auto Remove Filled for a clean chart.
Optional Labels showing the smoothed RSI value stored at the gap’s birth.
RSI-based Filters : only accept bullish gaps when RSI is oversold and bearish gaps when RSI is overbought.
Performance Controls : cap how many FVGs to keep on chart.
Alerts : new bullish or bearish FVG, filled FVG, and extreme RSI FVGs.
How it works
Source for Momentum : choose Returns, Close, or Volume.
Returns computes percent change over a short lookback to focus on impulse quality.
RSI and Smoothing : RSI length and a small SMA smooth the signal to stabilize the color coding.
Gap Scan : each bar checks for a 3-candle bullish or bearish imbalance that also clears your minimum size filter in percent of price.
Heatmap Color : the gap is painted at creation with a color from your palette based on the smoothed RSI value, preserving the momentum signature that formed it.
Lifecycle : if Extend Unfilled is on, the zone projects forward until price fully trades through the far edge. If Auto Remove is on, a filled gap is deleted immediately.
How to use it
Scan for structure : turn on both bullish and bearish FVGs. Start with a moderate Min FVG Size percent to reduce noise. You will see stacked clusters in trends and scattered singletons in chop.
Read the colors : brighter or stronger palette values imply stronger momentum at gap formation. Weakly colored gaps are lower conviction.
Decide bias : bullish FVGs below price suggest demand footprints. Bearish FVGs above price suggest supply footprints. Use the heatmap and RSI value to rank importance.
Choose your playbook :
Mean reversion : target partial or full fills of opposing FVGs that were created on weak momentum or that sit against higher timeframe context.
Trend continuation : look for price to respect the near edge of a strong-momentum FVG, then break away in the direction of the original impulse.
Manage risk : in continuation ideas, invalidation often sits beyond the opposite edge of the active FVG. In reversion ideas, invalidation sits beyond the gap that should attract price.
Two trade playbooks
Continuation - Buy the hold of a bullish FVG
Context uptrend.
A bullish FVG prints with strong RSI color.
Price revisits the top of the gap, holds, and rotates up. Enter on hold or first higher low inside or just above the gap.
Invalidation: below the gap bottom. Targets: prior swing, measured move, or next LV area.
Reversion - Fade a weak bearish FVG toward fill
Context range or fading trend.
A bearish FVG prints with weak RSI color near a completed move.
Price fails to accelerate lower and rotates back into the gap.
Enter toward mid-gap with confirmation.
Invalidation: above gap top. Target: opposite edge for a full fill, or the gap midline for partials.
Key settings
Max FVG Display : memory cap to keep charts fast. Try 30 to 60 on intraday.
Min FVG Size % : sets a quality floor. Start near 0.20 to 0.50 on liquid markets.
RSI Length and Smooth : 14 and 3 are balanced. Increase length for higher timeframe stability.
RSI Source :
Returns : most sensitive to true momentum bursts
Close : traditional.
Volume : uses raw volume impulses to judge footprint strength.
Filter by RSI Extremes : tighten rules so only the most stretched gaps print as signals.
Heatmap Style and Palette : pick a palette with good contrast for your background. Gradient for continuous feel, Discrete for quick zoning, Simple for binary, Palette for scientific schemes.
Extend Unfilled - Auto Remove : choose live projection and cleanup behavior to match your workflow.
Reading the chart
Bullish zones sit beneath price. Respect and hold of the upper boundary suggests demand. Strong green or warm palette tones indicate impulse quality.
Bearish zones sit above price. Respect and hold of the lower boundary suggests supply. Strong red or cool palette tones indicate impulse quality.
Stacking : multiple same-direction gaps stacked in a trend create ladders. Ladders often act as stepping stones for continuation.
Overlapping : opposing gaps overlapping in a small region usually mark a battle zone. Expect chop until one side is absorbed.
Workflow tips
Map higher timeframe trend first. Use lower timeframe FVGs for entries aligned with the higher timeframe bias.
Increase Min FVG Size percent and RSI length for noisy symbols.
Use labels when learning to correlate the RSI numbers with your palette colors.
Combine with VWAP or moving averages for confluence at FVG edges.
If you see repeated fills and refills of the same zone, treat that area as fair value and avoid chasing.
Alerts included
New Bullish FVG
New Bearish FVG
Bullish FVG Filled
Bearish FVG Filled
Extreme Oversold FVG - bullish
Extreme Overbought FVG - bearish
Practical defaults
RSI Length 14, Smooth 3, Source Returns.
Min FVG Size 0.25 percent on liquid majors.
Heatmap Style Gradient, Palette Viridis or Turbo for contrast.
Extend Unfilled on, Auto Remove on for a clean live map.
Notes
This tool does not predict the future. It maps imbalances and momentum so you can frame trades with clearer context, cleaner invalidation, and better ranking of which gaps matter. Use it with risk control and in combination with your broader process.
SEVENX Free|SuperFundedSEVENX — Modular Multi-Signal Scanner (SuperFunded)
What it is
SEVENX combines seven classic signals—MACD, OBV, RSI, Stochastics, CCI, Momentum, and an optional ATR volatility filter—into a modular gate. You can toggle each condition on/off, and a BUY/SELL arrow prints only when all enabled conditions agree. Text labels are optional.
Why this is not a simple mashup
・Most “combo” scripts just overlay indicators. SEVENX is a strict consensus engine:
・Each condition is binary and user-switchable.
・The final signal is the logical AND of all enabled checks (no hidden weights).
・Signals fire only on confirmed events (e.g., RSI crossing a level, Stoch K/D cross), which makes entries rule-driven and reproducible.
This yields a transparent, vendor-grade workflow where traders can start simple (2–3 gates) and tighten selectivity by enabling more gates.
How it works (concise)
・MACD: macd_line > signal_line (buy) / < (sell).
・OBV trend: OBV > OBV_MA (buy) / < (sell).
・RSI bounce/drop: crossover(RSI, Oversold) (buy) / crossunder(RSI, Overbought) (sell).
・Stoch cross: %K crosses above %D (buy) / below (sell).
・CCI rebound/pullback: crossover(CCI, -Level) (buy) / crossunder(CCI, +Level) (sell).
・Momentum: Momentum > 0 (buy) / < 0 (sell).
・ATR filter (optional): ATR > ATR_MA must also be true (both sides).
・Final signal: AND of all enabled conditions. If you enable none on a side, that side will not print.
Parameters (UI mapping)
Buy Signal (group: “— Buy Signal —”)
・MACD Golden Cross / OBV Uptrend / RSI Bounce from Oversold / Stochastic Golden Cross / CCI Rebound from Oversold / Momentum > 0 / ATR Volatility Filter (on/off)
Sell Signal (group: “— Sell Signal —”)
・MACD Dead Cross / OBV Downtrend / RSI Drop from Overbought / Stochastic Dead Cross / CCI Pullback from Overbought / Momentum < 0 / ATR Volatility Filter (on/off)
Indicator Settings
・MACD: Fast/Slow/Signal lengths.
・RSI: Length, Overbought/Oversold levels.
・Stochastics: %K length, %D smoothing, overall smoothing.
・CCI: Length, Level (±Level used).
・Momentum: Length.
・OBV: MA length for trend baseline.
・ATR: ATR length, ATR MA length (for the filter).
Display
・Show Text (BUY/SELL text on the markers), Buy/Sell Text Colors.
Practical usage
・Start simple: Enable 2 conditions (e.g., MACD + RSI). If signals are too frequent, add OBV or Momentum; if still frequent, enable ATR filter.
・Mean-reversion vs trend:
・For trend-following, prefer MACD/OBV/Momentum gates.
・For reversal bounces, add RSI/CCI gates and keep Stoch for timing.
・Tuning sensitivity:
・Raise RSI Oversold/Overbought thresholds to make bounces rarer.
・Increase ATR_MA length to smooth the volatility baseline.
・Risk first: Plan SL/TP independently (e.g., structure levels or R-multiples). SEVENX focuses on entry qualification, not exits.
Repainting & confirmation
Signals depend on cross events and are best treated on bar close. Intrabar flips can occur before a bar closes; for strict rules, confirm on closed bars in your strategy.
Disclaimer
No indicator can guarantee outcomes. News, liquidity, and spread conditions can invalidate signals. Trade responsibly and manage risk.
This indicator is being released on a trial basis and may be discontinued at our discretion.
SEVENX — モジュラー型マルチシグナル・スキャナー（日本語）
概要
SEVENXは、MACD / OBV / RSI / ストキャス / CCI / モメンタム / ATRフィルターの7条件を個別オン・オフで制御し、有効化した条件がすべて満たされたときだけBUY/SELL矢印を表示する、合意（AND）型シグナルインジです。テキスト表示も任意。
独自性・新規性
・各条件はブラックボックスではなく明示的なブール判定で、最終シグナルは有効化した条件のAND。
・RSIのレベルクロスやStochのK/Dクロスなど、確定イベントで判定するため、再現性の高いルール運用が可能。少数条件から始めて、必要に応じて段階的に厳格化できます。
動作要点
・MACD：線がシグナル上/下。
・OBV：OBVがOBVのMAより上/下。
・RSI：RSIがOSを上抜け（買い）/OBを下抜け（売り）。
・Stoch：%Kが%Dを上抜け/下抜け。
・CCI：CCIが**−Levelを上抜け**（買い）/+Levelを下抜け（売り）。
・Momentum：0より上/下。
・ATRフィルター（任意）：ATR > ATR_MA を満たすこと（買い/売り共通）。
・最終サイン：有効化した条件のAND。そのサイドで1つも有効化していなければサインは出ません。
実践ヒント
・まずは2条件（例：MACD＋RSI）でテスト → 多すぎるならOBV/MomentumやATRフィルターを追加。
・トレンド重視：MACD/OBV/Momentumを主軸に。
・押し目・戻り目狙い：RSI/CCIを追加、Stochでタイミング調整。
・感度調整：RSIのOB/OSを広げる、ATR_MAを長くする等で厳しめに。
・出口は別設計：SL/TPは価格帯やR倍数などで管理を。
再描画と確定
確定足基準で判断すると安定します。足確定前はクロスが行き来することがあります。
免責
シグナルの機能は保証されません。イベントや流動性で無効化する場合があります。資金管理のうえ自己責任でご利用ください。
このインジケーターは試験公開のため、弊社の裁量で公開を停止する場合があります。
PulseGrid Universal Scalper - Adaptive Pulse and Symmetric SpansInstrument agnostic. Works on any symbol and timeframe supported by TradingView.
Message or hit me up in chat for full access .
Purpose and scope
PulseGrid is a short timeframe strategy designed to read intrabar structure and recent path so that entries align with actionable momentum and context. The strategy is private. The description below provides all the information needed to understand how it behaves, how it sizes risk, how to tune it responsibly, and how to evaluate results without making unrealistic claims. The design is instrument agnostic. It runs on any asset class that prints open high low close bars on TradingView. That includes commodities such as Gold and WTI, currencies, crypto, equity indices, and single stocks. Performance will always depend on the symbol’s liquidity, spread, slippage, and session structure, which is why the description focuses on principles and safe parameter ranges instead of hard promises.
What the strategy does at a glance
It builds a composite entry signal named Pulse from five normalized bar features that reflect short term pressure and follow through.
It applies regime guards that keep the strategy inactive when the tape is either too quiet, too bursty, or too directionally random.
It optionally uses a directional filter where a fast and a slow exponential average must agree and their gap must be material relative to recent true range.
When a signal is allowed, risk is sized using symmetric spans that come from nearby untraded price distances above and below the market. The strategy sets a single stop and a single take profit from those spans.
Lines for entry, stop, and take profit are drawn on the chart. A compact on chart table shows trade counts, win rate, average R per trade, and profit factor for all trades, longs only, and shorts only.
This combination yields entries that are reactive but not chaotic, and risk lines that respect the market’s recent path instead of generic pip or point targets.
Why the design is original and useful
The core originality is the union of a composite entry that adapts to volatility and a geometry based risk model. The entry uses five different viewpoints on the same bar space instead of relying on a single technical indicator. The risk model uses spans that come from actual untraded distance rather than fixed multipliers of a generic volatility measure. The result is a framework that is simple to read on a chart and simple to evaluate, yet it avoids the traps of curve fitting to one symbol or one month of data. Because everything is normalized locally, the same logic translates across asset classes with only modest tuning.
The Pulse composite in detail
Pulse is a weighted blend of the following normalized features.
Impulse imbalance. The script sums upward and downward impulses over a short window. An upward impulse is the extension of highs relative to the prior bar. A downward impulse is the extension of lows relative to the prior bar. The net imbalance, scaled by the local range, captures whether extension pressure is building or fading.
Wick and close location. Inside each bar, the distance between the close and the extremes carries information about rejection or acceptance. A bar that closes near the high with relatively heavier lower wick suggests upward acceptance. A bar that closes near the low with heavier upper wick suggests downward acceptance. A weight controls the contribution of wick skew versus close location so that users can favor reversal or momentum behaviour.
Shock touches. Within the recent range window, touches that occur very near the top decile or bottom decile are marked. A short sliding window counts recent shocks. Frequent top shocks in a rising context suggest supply tests. Frequent bottom shocks in a declining context suggest demand tests. The count is normalized by window length.
Breakout ledger. The script compares current extremes to lagged extremes and keeps a simple count of recent upside and downside breakouts. The difference behaves as a short term polarity meter.
Curvature. A simple second difference in closing price acts as a curvature term. It is normalized by the recent maximum of absolute one bar returns so that the value remains bounded and comparable to other terms.
Pulse is smoothed over a fraction of the main signal length. Smoothing removes impulse spikes without destroying the quick reaction that scalpers need. The absolute value of smoothed Pulse can be used with an adaptive gate so that only the top percentile of energy for the recent environment is eligible for entries. A small floor prevents accidental entries during very quiet periods.
Regime guards that keep the strategy selective
Three guards must all pass before any entry can occur.
Auction Balance Factor. This is the proportion of closes that land inside a mid band of the prior bar’s high to low range. High values indicate balanced chop where breakouts tend to fail. Low values indicate directional conditions. The strategy requires ABF to sit below a user chosen maximum.
Dispersion via a Gini style measure on absolute returns. Very low dispersion means bars are small and uniform. Very high dispersion means a few outsized bars dominate and slippage risk can be elevated. The strategy allows the user to require the dispersion measure to remain inside a band that reflects healthy activity.
Binary entropy of direction. Over the core window, the proportion of up closes is used to compute a simple entropy. Values near one indicate coin flip behaviour. Values near zero indicate one sided sequences. The guard requires entropy below a ceiling so that random directionality does not produce noise entries.
An optional directional filter asks that a fast and a slow exponential average agree on direction and that their gap, when divided by an average true range, exceed a threshold. This filter can be enabled on symbols that trend cleanly and disabled when the composite entry is already selective enough.
Risk sizing with symmetric spans
Instead of fixed points or a pure ATR multiplier, the strategy sizes stops and targets from a pair of spans. The upward span reflects recent untraded distance above the market. The downward span reflects recent untraded distance below the market. Each span is floored by a fallback that comes from the maximum of a short simple range average and a standard average true range. A tick based floor prevents microscopic stops on instruments with high tick precision. An asymmetry cap prevents one span from becoming many times larger than the other. For long entries the stop is a multiple of the downward span and the target is a multiple of the upward span. For short entries the stop is a multiple of the upward span and the target is a multiple of the downward span. This creates a risk box that is symmetric by construction yet adaptive to recent voids and gaps.
Execution, ties, and housekeeping
Entries evaluate at bar close. Exits are tested from the next bar forward. If both stop and target are hit within the same bar, the outcome can be resolved in a consistent way that favors the stop or the target according to a single user setting. A short cooldown in bars prevents flip flops. Users can restrict entries to specific sessions such as London and New York. The chart renders entry, stop, and target lines for each trade so that every action is visible. The table in the top right shows trade counts, take profit and stop counts, win rate, average R per trade, and profit factor for the whole set and by direction.
Defaults and responsible backtesting
The default properties in the script use a realistic initial capital and commission value. Users should also set slippage in the strategy properties to reflect their broker and symbol. Small timeframe trading is sensitive to friction and the strategy description does not claim immunity to that reality. The strategy is intended to be tested on a dataset that produces a meaningful sample of trades. A sample in the range of a hundred trades or more is preferred because variance in short samples can be large. On thin symbols or periods with little regular trading, users should either change timeframe, change sessions, or use more selective thresholds so that the sample contains only liquid scenarios.
Universal usage across markets
The strategy is universal by design. It will run and produce lines on any open high low close series on TradingView. The composite entry is made of normalized parts. The regime guards use proportions and bounded measures. The spans use untraded distance and range floors measured in the local price scale. This allows the same logic to function on a currency pair, a commodity, an index future, a stock, or a crypto pair. What changes is calibration.
A safe approach for universal use is as follows.
Start with the default signal length and wick weight.
If the chart prints many weak signals, enable the directional filter and raise the normalized gap threshold slightly.
If the chart is too quiet, lower the adaptive percentile or, with adaptive off, lower the fixed pulse threshold by a small amount.
If stops are too tight in quiet regimes, raise the fallback span multiplier or raise the minimum tick floor in ticks.
If you observe long one sided days, lower the maximum entropy slightly so that entries only occur when directionality is genuine rather than alternating.
Because the logic is bounded and local, these simple steps carry over across symbols. That is why the strategy can be used literally on any asset that you can load on a TradingView chart. The code does not depend on a specific tick size or a specific exchange calendar. It will still remain true that symbols with higher spread or fewer regular trading hours demand stricter thresholds and larger floors.
Suggested parameter ranges for common cases
These ranges are guidelines for one to five minute bars. They are not promises of performance. They reflect the balance between having enough signals to learn from and keeping noise controlled.
Signal length between 18 and 34 for liquid commodities and large capitalization equities.
Wick weight between 0.30 and 0.50 depending on whether you want reversal recognition or close momentum.
Adaptive gate percentile between 85 and 93 when adaptive is enabled. Fixed threshold between 0.10 and 0.18 when adaptive is disabled. Use a non zero floor so very quiet periods still require some energy.
Auction Balance Factor maximum near 0.70 for symbols with clear session bursts. Slightly higher if you prefer to include more balanced prints.
Dispersion band with a lower bound near 0.18 and an upper bound near 0.68 for most session instruments. Tighten the band if you want to skip very bursty days or very flat days.
Entropy maximum near 0.90 so coin flip phases are filtered. Lower the ceiling slightly if the symbol whipsaws frequently.
Stop multiplier near one and take profit multiplier between two and three for a single target approach. Larger target multipliers reduce hit rate and lengthen holding time.
These are safe starting points across commodities, currencies, indices, equities, and crypto. From there, small increments are preferred over dramatic changes.
How to evaluate responsibly
A clean chart and a direct test process help avoid confusion. Use standard candles for signals and exits. If you use a non standard chart type such as Heikin Ashi or Renko, do so only for visualization and not for the strategy’s signal computation, as those chart types can produce unrealistic fills. Turn off other indicators on the published chart unless they are needed to demonstrate a specific property of this strategy. When you post results or discuss outcomes, include the symbol, timeframe, commission and slippage settings, and the session settings used. This makes the context clear and avoids misleading readers.
When you look at results, consider the following.
The distribution of R per trade. A positive average R with a moderate profit factor suggests that exits are sized appropriately for the symbol.
The balance between long and short sides. The HUD table separates the two so you can see if one side carries the edge for that symbol.
The sensitivity to the tie preference. If many bars hit both stop and take profit, the market is chopping inside the risk box and you may need larger floors or stricter regime guards.
The session effect. Session hours matter for many instruments. Align your session filter with where liquidity and volatility concentrate.
Known limitations and honest warnings
PulseGrid is not a guarantee of future profit. It is a systematic way to read short term structure and to size risk in a way that reflects recent path. It assumes that the data feed reflects the exchange reality. It assumes that slippage and spread are non zero and uses explicit commission and user provided slippage to approximate that. It does not place multiple targets. It does not trail stops. It is not a high frequency system and does not attempt to model queue priority or microsecond fills. On illiquid symbols or very short timeframes outside regular hours, signals will be less reliable. Users are responsible for choosing realistic settings and for evaluating whether the symbol’s conditions are suitable.
First use checklist
Load the symbol and timeframe you care about.
If the instrument has clear sessions, turn on the session filter and select realistic London and New York hours or other sessions relevant to the instrument.
Set commission and slippage in the strategy properties to values that match your broker or exchange.
Run the strategy with defaults. Look at the HUD summary and the lines.
Decide whether to enable the directional filter. If you see frequent reversals around the entry line, enable it and raise the normalized gap threshold slightly.
Adjust the adaptive gate. If the chart floods, raise the percentile. If the chart starves, lower it or use a slightly lower fixed threshold.
Adjust the fallback span multiplier and tick floor so that stops are never microscopic.
Review per session performance. If one session underperforms, restrict entries to the better one.
This simple process takes minutes and transfers to any other symbol.
Why this script is private
The source remains private so that the underlying method and its implementation details are not copied or republished. The description here is complete and self contained so that users can understand the purpose, originality, usage, and limitations without needing to inspect the source. Privacy does not change the strategy’s on chart behavior. It only protects the specific coding details.
Guarantee and compliance statements
This description does not contain advertising, solicitations, links, or contact information. It does not make performance promises. It explains how the script is original and how it works. It also warns about limitations and the need for realistic assumptions. The strategy is not investment advice and is not created only for qualified investors. It can be tested and used for educational and research purposes. Users should read TradingView’s documentation on script properties and backtesting. Users should avoid non standard chart types for signal computation because those produce unrealistic results. Users should select realistic account sizes and friction settings. Users should not post claims without showing the settings used.
Closing summary
PulseGrid is a compact framework for short timeframe trading that combines a composite entry built from multiple normalized bar features with a symmetric span model for risk. The entry adapts to volatility. The regime guards keep the strategy inactive when the tape is either too quiet or too erratic. The risk geometry respects recent untraded spans instead of arbitrary distances. The entire design is instrument agnostic. It will run on any symbol that TradingView supports and it will behave consistently across asset classes with modest tuning. Use it with a clean chart, realistic friction, and enough trades to make your evaluation meaningful. Use sessions if the instrument concentrates activity in specific hours. Adjust one control at a time and prefer small increments. The goal is not to find a magic parameter. The goal is to maintain a stable rule set that reads market structure in a way you can trust and audit.
Markov Chain Regime & Next‑Bar Probability Forecast✨ What it is
A regime-aware, math-driven panel that forecasts the odds for the very next candle. It shows:
• P(next r > 0)
• P(next r > +θ)
• P(next r < −θ)
• A 4-bucket split of next-bar outcomes (>+θ | 0..+θ | −θ..0 | <−θ)
• Next-regime probabilities: Calm | Neutral | Volatile
🧠 Why the math is strong
• Markov regimes: Markets cluster in volatility “moods.” We learn a 3-state regime S∈{Calm, Neutral, Volatile} with a transition matrix A, where A = P(Sₜ₊₁=j | Sₜ=i).
• Condition on the future state: We estimate event odds given the next regime j—
q_pos(j)=P(rₜ₊₁>0 | Sₜ₊₁=j), q_gt(j)=P(rₜ₊₁>+θ | Sₜ₊₁=j), q_lt(j)=P(rₜ₊₁<−θ | Sₜ₊₁=j)—
and mix them with transitions from the current (or frozen) state sNow:
P(event) = Σⱼ A · q(event | j).
This mixture-of-regimes view (HMM-style one-step prediction) ties next-bar outcomes to where volatility is likely headed.
• Statistical hygiene: Laplace/Beta smoothing, minimum-sample gating, and unconditional fallbacks keep estimates stable. Heavy computations run on confirmed bars; “Freeze at close” avoids intrabar flicker.
📊 What each value means
• Regime label & background: 🟩 Calm, 🟧 Neutral, 🟥 Volatile — quick read of market context.
• P(next r > 0): Directional tilt for the very next bar.
• P(next r > +θ): Odds of an outsized positive move beyond θ.
• P(next r < −θ): Odds of an outsized negative move beyond −θ.
• Partition row: Distributes next-bar probability across four intuitive buckets; they ≈ sum to 100%.
• Next Regime Probs: Likelihood of switching to Calm/Neutral/Volatile on the next bar (row of A for the current/frozen state).
• Samples row: How many next-bar samples support each next-state estimate (a confidence cue).
• Smoothing α: The Laplace prior used to stabilize binary event rates.
⚙️ Inputs you control
• Returns: Log (default) or %
• Include Volume (z-score) + lookback
• Include Range (HL/PrevClose)
• Rolling window N (transitions & estimates)
• θ as percent (e.g., 0.5%)
• Freeze forecast at last close (recommended)
• Display toggles (plots, partition, samples)
🎯 How to use it
• Volatility awareness & sizing: Rising P(next regime = Volatile) → consider smaller size, wider stops, or skipping marginal entries.
• Breakout preparation: Elevated P(next r > +θ) highlights environments where range expansion is more likely; pair with your setup/trigger.
• Defense for mean-reversion: If P(next r < −θ) lifts while you’re late long (or P(next r > +θ) lifts while late short), tighten risk or wait for better context.
• Calibration tip: Start θ near your market’s typical bar size; adjust until “>+θ” flags truly meaningful moves for your timeframe.
📝 Method notes & limits
Activity features (|r|, volume z, range) are standardized; only positive z’s feed the composite activity score. Estimates adapt to instrument/timeframe; rare regimes or small windows increase variance (hence smoothing, sample gating, fallbacks). This is a context/forecast tool, not a standalone signal—combine with your entry/exit rules and risk management.
🧩 Strategies too
We also develop full strategy versions that use these probabilities for entries, filters, and position sizing. Like this publication if you’d like us to release the strategy edition next.
⚠️ Disclaimer
Educational use only. Not financial advice. Markets involve risk. Past performance does not guarantee future results.
Ultimate Stock Trend & Liquidity Screener1. Overview & Originality
This script is a comprehensive, all-in-one screening tool designed to identify high-quality, trend-following opportunities in global stock markets. Its originality lies in combining seven distinct logical checks—spanning liquidity, trend, momentum, and volatility—into a single, cohesive framework.
www.tradingview.com
The script's core innovation is its "Total Score" system. This feature moves beyond simple binary filtering by quantifying how well a stock meets the ideal criteria for a tradable trend. This allows you to rank entire watchlists to find the most promising candidates, not just the ones that meet a minimum threshold.
Designed for full integration with the TradingView ecosystem, the script outputs all individual conditions and the Total Score as separate columns in the Pine Screener, enabling deep and flexible market analysis.
2. Core Concepts & How It Works
Built on the classic principles of trend-following, this screener validates potential trades against a robust checklist. The default parameters are tuned for stock market analysis, using standard lookback periods like the 50 and 200-day moving averages.
The script systematically checks for:
Liquidity: Guarantees the stock is actively traded by filtering for minimum daily dollar volume (turnover) and a healthy 30-day average volume, which is critical for good execution.
Trend Confirmation: Employs the classic 50/200 Simple Moving Average "golden cross" structure to confirm a healthy, long-term uptrend.
Trend Quality: Includes an optional filter to verify that the long-term 200-day SMA is actively sloping upwards, ensuring the underlying trend has momentum.
Trend Strength: Uses the Average Directional Index (ADX) to filter out weak or sideways markets, focusing only on stocks in a strong, established trend.
Momentum: Confirms the trend is supported by sustained buying pressure by checking that the Relative Strength Index (RSI) is in a bullish regime (above 50).
Volatility: Requires a minimum level of volatility using the Average True Range (ATR) as a percentage of the price, ensuring the stock has enough movement to be tradable.
Strategic Entry: Offers a user-selectable "Entry Mode" to fit different trading styles:
Breakout Mode: Identifies stocks hitting new highs on a surge of volume.
Pullback Mode: Finds stocks already in a strong uptrend that are experiencing a healthy dip to a short-term moving average.
3. How to Use This Script
This indicator is designed for two primary workflows:
Single-Stock Analysis: Apply the script to any stock chart to see a detailed diagnostic table in the bottom-right corner. This table provides a real-time checklist for all 7 conditions and the Total Score.
Full Market Screening (Recommended):
Open the Stock Screener on TradingView.
Click "Filters" and select this script from the Pine Screener menu.
Click the "Columns" button and add the new columns generated by this script ("Total Score," "Liquidity OK," etc.).
You can now sort your entire watchlist by "Total Score" to find the best candidates or filter for stocks that meet a minimum score (e.g., Total Score > 5 ).
4. Inputs & Customization
All parameters are fully customizable in the script's "Settings" menu. You can easily adjust moving average lengths, thresholds, and lookback periods to tailor the screener to your specific strategy, timeframe, or market.
5. Disclaimer
This tool is for educational and analytical purposes only. It is not financial advice and does not guarantee any specific outcome or profit. Past performance is not indicative of future results. Always use this screener as part of a complete trading plan that includes your own analysis and risk management.
Ultimate Crypto Trend & Liquidity Screener v11. Overview & Originality
This script is an advanced, all-in-one screening tool designed specifically to identify high-potential, trend-following opportunities within the cryptocurrency market. While many screeners focus on single conditions, the "Ultimate Crypto Trend & Liquidity Screener" is original in its multi-layered approach, combining seven distinct logical checks into a single, cohesive framework.
Its primary innovation is the calculation of a "Total Score," which quantifies how well an asset conforms to the ideal characteristics of a tradable trend. This allows traders to move beyond simple binary (yes/no) filtering and instead rank the entire market to find the absolute best candidates that match their strategy.
The script is fully compatible with the TradingView Pine Screener, outputting each individual condition and the Total Score as separate columns for powerful, flexible market analysis.
2. Core Concepts & How It Works
This screener is built on the core principles of classic trend-following. It evaluates assets against a comprehensive checklist to ensure they are not only trending, but are also liquid, volatile, and at a strategic entry point.
The script systematically checks for:
Liquidity: Ensures the asset is actively traded with significant dollar volume, which is crucial for minimizing slippage. It checks both the daily turnover and the 30-day average volume.
Trend Confirmation: Utilizes a dual-moving average system (20/50 SMA default) to confirm the underlying trend direction. It also includes an optional filter to ensure the long-term moving average is actively sloping upwards, confirming trend health.
Trend Strength: Employs the Average Directional Index (ADX) to measure the strength of the trend, filtering out weak or choppy price action.
Momentum: Uses the Relative Strength Index (RSI) to confirm that the asset has positive momentum, as strong trends are supported by sustained buying pressure.
Volatility: Measures volatility using the Average True Range (ATR) as a percentage of the price. This ensures the asset has enough movement to be profitable, a key factor in the 24/7 crypto market.
Strategic Entry: Offers a user-selectable "Entry Mode." You can choose between:
Breakout Mode: Identifies assets breaking out to new highs on a surge of volume.
Pullback Mode: Identifies assets already in a strong uptrend that are experiencing a healthy dip to a key moving average, offering a potentially better risk/reward entry.
3. How to Use This Script
This indicator is designed for two primary workflows:
Single-Asset Analysis: When you apply the script to any crypto chart, a detailed diagnostic table will appear in the bottom-right corner. This table provides a real-time checklist, showing true or false for each of the 7 conditions and the final score, allowing for a quick and deep analysis of any individual asset.
Full Market Screening (Recommended):
Open the Crypto Screener on TradingView.
Click the "Filters" button and at the bottom of the menu, select this script ("Ultimate Crypto Trend & Liquidity Screener").
Click the "Columns" button on the screener and add the columns generated by this script, such as "Total Score," "Liquidity OK," "Entry Signal OK," etc.
You can now sort the entire crypto market by "Total Score" to instantly find the strongest candidates, or filter for assets that meet specific conditions (e.g., Total Score > 5 ).
4. Inputs & Customization
All parameters within this script are fully customizable via the "Settings" menu. The default values have been tuned for general use in the crypto market (e.g., faster moving averages, higher volatility thresholds), but you are encouraged to adjust them to fit your specific trading style, preferred timeframes, and risk tolerance.
5. Disclaimer
This tool is designed for educational and analytical purposes to aid in the decision-making process. It does not provide financial advice or guarantee trading success. Past performance is not indicative of future results. Always use this screener in conjunction with your own comprehensive analysis and robust risk management practices. This script is published open-source to encourage community learning and collaboration.
Aggregation Index SmoothedAggregation Index Smoothed (AIS) - Multi-Method Trend Consensus Oscillator
What This Indicator Does
The Aggregation Index Smoothed combines four independent trend-detection methodologies into a unified momentum oscillator that operates across multiple timeframes simultaneously. Unlike traditional single-method indicators that can produce conflicting or false signals during market transitions, AIS requires consensus agreement across all four calculation methods before confirming trend direction.
Technical Methodology
Four-Component Loop System
Each component analyzes 16 different lookback periods (default range: 5-20 bars), creating a multi-timeframe perspective within a single calculation:
1. Price Change Analysis
Measures directional price movement across all periods. Each period scores +1 for positive change or -1 for negative change. Results are averaged and scaled to ±100.
2. RSI Multi-Period Analysis
Evaluates Relative Strength Index values across the same 16 periods. Scores +1 when RSI > 50 (momentum favoring bulls) or -1 when RSI < 50 (momentum favoring bears). This captures overbought/oversold conditions across multiple timeframes.
3. EMA Trend Position
Compares current price against Exponential Moving Averages of varying lengths (5-20 periods). Scores +1 when price trades above EMA (uptrend) or -1 when below (downtrend). This identifies trend alignment across short, medium, and longer-term moving averages.
4. Momentum Rate-of-Change
Calculates price momentum across all periods using the mom() function. Scores +1 for positive momentum or -1 for negative momentum, detecting acceleration and deceleration patterns.
Aggregation Process
Each of the four indicators independently calculates scores across all 16 periods
Individual indicator scores are averaged (range: -100 to +100)
All four indicator averages are combined using arithmetic mean
The resulting index undergoes EMA smoothing (default: 20 periods)
Optional double-smoothing applies a second EMA pass for maximum noise reduction
Why This Approach Is Unique
Problem Solved: Traditional oscillators often conflict - RSI might be bullish while MACD is bearish, or stochastic shows oversold while price trend is clearly down. Traders waste time reconciling these contradictions.
Solution: AIS eliminates conflicts by design. A bullish signal (+10 threshold) means all four methods across all 16 timeframes agree on upward momentum. This consensus approach dramatically reduces whipsaws and false signals compared to using any single method.
Technical Advantage: The for-loop methodology validates each signal across a spectrum of timeframes (5 bars through 20 bars), ensuring the trend is confirmed in both immediate-term and intermediate-term contexts. This is mathematically equivalent to running 64 separate indicators (4 methods × 16 periods) and requiring majority agreement.
Signal Generation
Long Signal (Bullish): Index crosses above +10 threshold
Indicates all four methods confirm upward momentum across multiple timeframes
Sustained readings above +10 suggest strong trend continuation
Short Signal (Bearish): Index crosses below -10 threshold
Indicates all four methods confirm downward momentum across multiple timeframes
Sustained readings below -10 suggest strong downtrend
Neutral Zone (-10 to +10): Mixed signals or consolidation
Methods disagree on direction, suggesting choppy or range-bound conditions
Avoid trend-following strategies in this zone
How to Use This Indicator
Best Practices
Timeframe Selection:
Most effective on 4-hour charts and higher (Daily, Weekly)
Lower timeframes (1H, 15m) may produce excessive signals despite smoothing
The 16-period loop range is optimized for swing trading timeframes
Entry Strategy:
Wait for index to cross threshold levels (±10)
Confirm with price action (breakout, support/resistance levels)
Consider entering on first pullback after threshold cross for better risk/reward
Parameter Adjustment:
Volatile instruments (crypto, small-caps): Increase thresholds to ±15 or ±20 to filter noise
Stable instruments (large-cap stocks, indices): Reduce thresholds to ±5 for earlier signals
Smoothing Length: Increase to 30+ for cleaner signals; decrease to 10-15 for faster response
Double Smoothing: Keep enabled for trend following; disable for more reactive signals
Risk Management:
Exit longs when index drops back into neutral zone (below +10)
Exit shorts when index rises into neutral zone (above -10)
Use index slope as trend strength indicator (steeper = stronger)
Interpretation Guidelines
Strong Trending Conditions:
Index sustained above +50 or below -50 indicates powerful directional move
All four methods showing extreme agreement across all timeframes
High probability of trend continuation
Trend Exhaustion Signals:
Index reaches extreme levels (+80 to +100 or -80 to -100)
Potential reversal zone; watch for divergence with price
Consider taking partial profits on existing positions
Divergence Detection:
Price makes new highs while index fails to confirm = bearish divergence
Price makes new lows while index shows higher lows = bullish divergence
Divergences on 4H+ timeframes carry significant weight
Limitations and Considerations
Not Suitable For:
Scalping or very short-term trading (under 1-hour timeframes)
Range-bound markets with no clear trend (index oscillates in neutral zone)
Instruments with erratic, news-driven price action
Known Lag:
Double smoothing introduces 40+ bar delay in signal generation
Designed for trend confirmation, not early trend detection
Fast market reversals may produce late exit signals
Complementary Tools:
Combine with support/resistance levels for entry precision
Use with volume analysis to confirm signal strength
Pair with volatility indicators (ATR) for position sizing
Technical Implementation Notes
The indicator pre-calculates all RSI and EMA values for lengths 5-20 to comply with Pine Script's requirement for constant-length parameters in ta.rsi() and ta.ema() functions. This workaround allows dynamic loop-based analysis while maintaining calculation consistency on every bar.
The scoring methodology uses binary classification (+1/-1) rather than normalized percentage values to ensure equal weighting across all four methods, preventing any single indicator from dominating the aggregate signal.
Summary: The Aggregation Index Smoothed provides trend confirmation through multi-method consensus across variable timeframes. Its primary value is eliminating the confusion of conflicting indicator signals by requiring agreement from four independent trend calculations before generating actionable signals. Best suited for swing traders and position traders on 4-hour and higher timeframes seeking high-probability trend-following entries with reduced false signals.
Value Spectrum | OquantOverview
The Value Spectrum is an indicator designed to provide traders with a visual and quantitative assessment of price positioning relative to a dynamic baseline, helping to identify potential value zones, overextensions, and fair value conditions in various market environments. It builds on traditional volatility envelope concepts but introduces multi-tiered bands with customizable smoothing and a spectrum-based classification system to offer a more nuanced view of market conditions. This allows traders to quickly gauge where price stands in its "value spectrum" without relying solely on binary overbought/oversold signals.
Key Factors/Components
Baseline: A selectable moving average that serves as the central reference point for the envelope.
Volatility Measure: Derived from standard deviation, with optional smoothing to reduce noise in choppy markets.
Multi-Level Bands: Six upper and lower bands are incremented with steps of 0.5x, creating a graduated spectrum rather than fixed thresholds.
Value Classification: A table that categorizes the current price position into distinct levels, such as fair value, oversold, or overbought, for at-a-glance analysis.
How It Works
The indicator calculates a baseline using the chosen moving average type applied to the selected source (e.g., close price). It then measures volatility through standard deviation over a specified length, which can be smoothed using methods like median or other averages to adapt to market noise. Bands are constructed by adding and subtracting multiples of this volatility from the baseline, forming a series of widening zones. Price is evaluated against these zones to determine its position in the spectrum—closer to the baseline suggests fair value, while farther out indicates increasing degrees of extension. The visual fills between bands use gradient transparency to highlight the progression, and the table updates in real-time to label the current state based on where price falls.
For Who It Is Best/Recommended Use Cases
This indicator is best suited for swing traders, and mean-reversion strategists who need to assess relative value mainly in ranging markets. Recommended use cases include:
Identifying entry points in oversold/overbought conditions.
Confirming fair value zones for holding positions or scaling in.
Monitoring extreme extensions as potential reversal warnings.
Settings and Default Settings
Source: Defines the input data series (default: close).
Select MA for Baseline: Choose from options like SMA, EMA, ALMA, HMA, WMA, LSMA, DEMA, TEMA, SMMA(RMA), FRAMA, ZLEMA, T3, VWMA, TRIMA (default: DEMA).
MA Length: Period for the baseline calculation (default: 30).
Alma Offset: Adjusts the offset for ALMA if selected (default: 0.85).
Alma Sigma: Sets the sigma for ALMA if selected (default: 4).
T3 Vol Factor: Volume factor for T3 if selected (default: 0.7).
SD Length: Period for volatility calculation (default: 21).
Smooth Volatility: Enables/disables volatility smoothing (default: false).
Select Volatility Smoothing Method: Options include MEDIAN, SMA, EMA, DEMA, WMA (default: MEDIAN).
Volatility Smoothing Length: Period for smoothing volatility if enabled (default: 20).
Show Table: Toggles the display of the value classification table (default: true).
Conclusion
The Value Spectrum offers a flexible and insightful way to visualize price in context, empowering traders to make informed decisions based on a structured assessment of market value. By customizing the baseline and volatility components, it adapts to different trading styles and assets, providing clarity in different conditions.
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
Aggregated Scores Oscillator [Alpha Extract]A sophisticated risk-adjusted performance measurement system that combines Omega Ratio and Sortino Ratio methodologies to create a comprehensive market assessment oscillator. Utilizing advanced statistical band calculations with expanding and rolling window analysis, this indicator delivers institutional-grade overbought/oversold detection based on risk-adjusted returns rather than traditional price movements. The system's dual-ratio aggregation approach provides superior signal accuracy by incorporating both upside potential and downside risk metrics with dynamic threshold adaptation for varying market conditions.
🔶 Advanced Statistical Framework
Implements dual statistical methodologies using expanding and rolling window calculations to create adaptive threshold bands that evolve with market conditions. The system calculates cumulative statistics alongside rolling averages to provide both historical context and current market regime sensitivity with configurable window parameters for optimal performance across timeframes.
🔶 Dual Ratio Integration System
Combines Omega Ratio analysis measuring excess returns versus deficit returns with Sortino Ratio calculations focusing on downside deviation for comprehensive risk-adjusted performance assessment. The system applies configurable smoothing to both ratios before aggregation, ensuring stable signal generation while maintaining sensitivity to regime changes.
// Omega Ratio Calculation
Excess_Return = sum((Daily_Return > Target_Return ? Daily_Return - Target_Return : 0), Period)
Deficit_Return = sum((Daily_Return < Target_Return ? Target_Return - Daily_Return : 0), Period)
Omega_Ratio = Deficit_Return ≠ 0 ? (Excess_Return / Deficit_Return) : na
// Sortino Ratio Framework
Downside_Deviation = sqrt(sum((Daily_Return < Target_Return ? (Daily_Return - Target_Return)² : 0), Period) / Period)
Sortino_Ratio = (Mean_Return / Downside_Deviation) * sqrt(Annualization_Factor)
// Aggregated Score
Aggregated_Score = SMA(Omega_Ratio, Omega_SMA) + SMA(Sortino_Ratio, Sortino_SMA)
🔶 Dynamic Band Calculation Engine
Features sophisticated threshold determination using both expanding historical statistics and rolling window analysis to create adaptive overbought/oversold levels. The system incorporates configurable multipliers and sensitivity adjustments to optimize signal timing across varying market volatility conditions with automatic band convergence logic.
🔶 Signal Generation Framework
Generates overbought conditions when aggregated score exceeds adjusted upper threshold and oversold conditions below lower threshold, with neutral zone identification for range-bound markets. The system provides clear binary signal states with background zone highlighting and dynamic oscillator coloring for intuitive market condition assessment.
🔶 Enhanced Visual Architecture
Provides modern dark theme visualization with neon color scheme, dynamic oscillator line coloring based on signal states, and gradient band fills for comprehensive market condition visualization. The system includes zero-line reference, statistical band plots, and background zone highlighting with configurable transparency levels.
snapshot
🔶 Risk-Adjusted Performance Analysis
Utilizes target return parameters for customizable risk assessment baselines, enabling traders to evaluate performance relative to specific return objectives. The system's focus on downside deviation through Sortino analysis provides superior risk-adjusted signals compared to traditional volatility-based oscillators that treat upside and downside movements equally.
🔶 Multi-Timeframe Adaptability
Features configurable calculation periods and rolling windows to optimize performance across various timeframes from intraday to long-term analysis. The system's statistical foundation ensures consistent signal quality regardless of timeframe selection while maintaining sensitivity to market regime changes through adaptive band calculations.
🔶 Performance Optimization Framework
Implements efficient statistical calculations with optimized variable management and configurable smoothing parameters to balance responsiveness with signal stability. The system includes automatic band adjustment mechanisms and rolling window management for consistent performance across extended analysis periods.
This indicator delivers sophisticated risk-adjusted market analysis by combining proven statistical ratios in a unified oscillator framework. Unlike traditional overbought/oversold indicators that rely solely on price movements, the ASO incorporates risk-adjusted performance metrics to identify genuine market extremes based on return quality rather than price volatility alone. The system's adaptive statistical bands and dual-ratio methodology provide institutional-grade signal accuracy suitable for systematic trading approaches across cryptocurrency, forex, and equity markets with comprehensive visual feedback and configurable risk parameters for optimal strategy integration.
MACD-V+MACD-V+ Indicator - Advanced Momentum Analysis
The MACD-V+ indicator is an enhanced version of the volatility-normalized MACD methodology developed by Alex Spiroglou. This approach addresses critical limitations of traditional MACD through ATR-based volatility normalization, providing comparable values across time and markets.
What is MACD-V?
MACD-V applies Average True Range (ATR) normalization to traditional MACD, creating a universal momentum indicator that works consistently across all markets and timeframes. The methodology was developed through extensive statistical research analyzing multiple markets and timeframes.
Formula: × 100
This normalization transforms MACD from price-dependent values into standardized momentum readings.
Traditional MACD Limitations
Limitation 1: Non-Comparable Values Across Time
Traditional MACD values cannot be compared across different time periods due to varying price levels. S&P 500 maximum MACD was 1.56 in 1957-1971, but reached 86.31 in 2019-2021 - not indicating 55x stronger momentum, but simply different price scales.
Solution: MACD-V provides comparable historical values where a reading of 100 today has the same mathematical meaning as 100 in any previous period.
Limitation 2: Non-Comparable Across Markets
Traditional MACD cannot compare momentum between different assets. S&P 500 MACD of 65 versus EUR/USD MACD of -0.5 reflects price differences, not relative strength.
Solution: MACD-V creates universal levels that work across all markets. The ±150 extreme levels apply consistently whether analyzing stocks, bonds, commodities, or currencies.
Limitation 3: No Objective Momentum System
Traditional MACD lacks universal overbought or oversold level definitions, making systematic analysis difficult.
Solution: MACD-V provides an objective 7-stage momentum lifecycle system with clearly defined zones and state transitions.
Limitation 4: Signal Line False Signals
In low momentum environments, traditional MACD generates multiple false signals as the line oscillates near zero.
Solution: MACD-V filters signal quality by identifying neutral zones (-50 to +50) where signal reliability is lower.
Limitation 5: Signal Line Timing Lag
During extreme momentum, traditional MACD signal line lags significantly due to large separation from the MACD line.
Solution: MACD-V anticipates timing issues in extreme momentum environments (±150) through zone-based analysis and lifecycle states.
Universal Application
MACD-V+ works across:
Individual Stocks
Forex Pairs
Commodity Futures
Cryptocurrencies
All Timeframes
Key Features
Zone System
Overbought Zone: Above +150 (extreme bullish momentum)
Rally Zone: +50 to +150 (strong bullish momentum)
Ranging Zone: -50 to +50 (neutral/low momentum)
Rebound Zone: -50 to -150 (strong bearish momentum)
Oversold Zone: Below -150 (extreme bearish momentum)
7-Stage Lifecycle States
Ranging: Neutral momentum in -50 to +50 zone
Rallying: Rally zone + MACD above Signal + rising momentum
Overbought: Extreme zone above +150
Retracing: Rally zone + MACD below Signal (pullback from overbought)
Reversing: Rebound zone + MACD below Signal + falling momentum
Oversold: Extreme zone below -150
Rebounding: Rebound zone + MACD above Signal (recovery from oversold)
Visual Status Display
Real-Time State Table: Shows current lifecycle state name
Color-Coded States: Blue (Rallying/Rebounding), Red (Overbought/Oversold), Orange (Retracing/Reversing), Gray (Ranging)
Strength Multiplier: Live histogram strength indicator (e.g., "x 1.45")
Enhanced Features (Plus)
Absolute Histogram MA: ATR-length moving average of absolute histogram values for strength measurement
Direction-Aware Display: MA line follows histogram sign (positive above 0, negative below 0)
Strength Multiplier: Current momentum vs. average strength ratio (always positive value)
Histogram Extreme Levels: Short-term overbought/oversold (±40) for pullback detection
Chart Legend - Visual Signal Guide
Lines and Histogram
🔵 Blue Line: MACD-V value (ATR-normalized momentum)
🟠 Orange Line: Signal line (9-period EMA of MACD-V)
📊 Histogram Bars: MACD-V minus Signal line (momentum differential)
Histogram Colors: Green shades (positive momentum), Red shades (negative momentum)
🟡 Yellow Line: Dynamic MA of absolute histogram values (follows histogram sign)
Background Colors
🟥 Light Red Background: Extreme overbought zone (MACD-V > +150)
🟩 Light Green Background: Extreme oversold zone (MACD-V < -150)
Horizontal Reference Lines
➖ +150 (Gray Dashed): Overbought extreme level
➖ +50 (Gray Dashed): Rally zone entry level
➖ 0 (Gray Solid): Zero line - trend separator
➖ -50 (Gray Dashed): Rebound zone entry level
➖ -150 (Gray Dashed): Oversold extreme level
Optional Histogram Levels
➖ +40 (Yellow Dashed): Histogram short-term overbought
➖ -40 (Yellow Dashed): Histogram short-term oversold
Status Table
📋 Top-Center Table: Current lifecycle state display
State Name: RANGING / RALLYING / OVERBOUGHT / RETRACING / REVERSING / OVERSOLD / REBOUNDING
Histogram Warning: Short-term overbought/oversold alerts (±40 levels)
State Label
📊 Label at MACD/Signal Midpoint: Current lifecycle state with strength analysis
State Name: RANGING / RALLYING / OVERBOUGHT / RETRACING / REVERSING / OVERSOLD / REBOUNDING
Strength Multiplier Interpretation:
- Strong acceleration (>1.75): Powerful momentum, trend continuation likely
- Moderate progression (1.25-1.75): Normal trend strength
- Trend continuation (0.75-1.25): Stable momentum near average
- Watch for reversal (0.25-0.75): Weakening momentum
- Trend exhaustion (<0.25): Very weak momentum, reversal possible
Trading Applications
1. Lifecycle State Trading
Enter Long: When state changes to "RALLYING" (strong bullish momentum established)
Enter Short: When state changes to "REVERSING" (strong bearish momentum established)
Exit/Reduce: When state reaches "OVERBOUGHT" or "OVERSOLD" (extreme levels)
Avoid Trading: When state is "RANGING" (low momentum, unreliable signals)
2. Zone-Based Trading
Rally Zone (+50 to +150): Look for pullback entries (histogram dips)
Rebound Zone (-50 to -150): Look for bounce entries (histogram rises)
Extreme Zones (±150+): Prepare for reversal or take profits
Ranging Zone (-50 to +50): Wait for breakout confirmation
3. Signal Line Crossovers
Bullish Cross: MACD-V crosses above Signal line (momentum shift up)
Bearish Cross: MACD-V crosses below Signal line (momentum shift down)
Quality Filter: Trust crossovers in Rally/Rebound zones, ignore in Ranging zone
4. Zero Line Crosses
Cross Above 0: Transition to bullish regime
Cross Below 0: Transition to bearish regime
Trend Confirmation: Strong trends keep MACD-V on same side of zero
5. Histogram Extreme Strategy
Above +40: Short-term overbought - potential pullback
Below -40: Short-term oversold - potential bounce
Use with Trend: Buy dips to -40 in uptrend, sell rallies to +40 in downtrend
6. Strength Multiplier Analysis
> 1.75: Strong acceleration - powerful momentum, trend continuation highly likely
1.25 to 1.75: Moderate progression - normal healthy trend strength
0.75 to 1.25: Trend continuation - stable momentum near average strength
0.25 to 0.75: Watch for reversal - momentum weakening significantly
< 0.25: Trend exhaustion - very weak momentum, reversal possible
Comprehensive Alert System
Lifecycle State Change Alerts
Range Entered (low momentum warning)
Rally Started (bullish momentum established)
Overbought Reached (extreme bullish level)
Overbought Exit (leaving extreme zone)
Retracing Started (pullback from overbought)
Reversal Started (bearish momentum established)
Oversold Reached (extreme bearish level)
Oversold Exit (leaving extreme zone)
Rebounding Started (recovery from oversold)
Alert Builder Integration
Binary outputs (1/0) for external alert systems:
Individual state flags for each of 7 lifecycle states
Strength multiplier value for programmatic trend assessment
Settings & Parameters
MACD Configuration
MACD Fast: Fast EMA period (default: 12)
MACD Slow: Slow EMA period (default: 26)
Signal Line: Signal smoothing period (default: 9)
Source: Price source (default: Close)
Zone Boundaries
Overbought: Extreme bullish level (default: 150)
Oversold: Extreme bearish level (default: -150)
Rally: Strong bullish zone entry (default: 50)
Rebound: Strong bearish zone entry (default: -50)
Histogram Bounds
Histogram OB: Short-term overbought (default: 40)
Histogram OS: Short-term oversold (default: -40)
Trend Filters
MA Type: Histogram strength MA calculation method (None / SMA / EMA)
Show Elder Impulse Plus: Bar color system based on EMA(13) + histogram direction
200 EMA trend: Trend Filter v1 - Bull/Bear classification (adaptive MACD-V levels)
50/200 EMA 6-stage: Trend Filter v2 - Chuck Dukas Diamond 6-stage market classification
Best Practices
Trending Markets
Focus on "RALLYING" or "REVERSING" states for entries
Use histogram pullbacks (±40) for position additions
Monitor strength multiplier - exit if drops below 0.25
Take profits in extreme zones (±150+)
Yellow MA crossing histogram warns of momentum shift
Ranging Markets
Avoid trading when state is "RANGING"
Wait for clear zone entry (Rally/Rebound zone)
Use shorter timeframes for precision
Reduce position sizes due to lower reliability
Multi-Timeframe Analysis
Higher timeframe: Identify market regime (lifecycle state)
Lower timeframe: Time precise entries (histogram pullbacks)
Alignment: Trade only when both timeframes agree on direction
Risk Management
Reduce position size in extreme zones (±150+)
Use lifecycle state changes for stop-loss placement
Scale out of positions when strength multiplier < 0.25
Avoid counter-trend trades in strong states (RALLYING/REVERSING)
Watch yellow MA - when it crosses below histogram absolute value, momentum weakening
Combining with LBR 3/10-V Indicator
MACD-V+ and LBR 3/10-V create a powerful two-timeframe momentum system for strategic direction and tactical timing.
Strategic Filter: MACD-V+ determines WHETHER to trade (market regime)
Tactical Precision: LBR 3/10-V determines WHEN to enter (timing)
Double Confirmation: Both indicators must agree on direction
Lifecycle Management: Exit when MACD-V+ state changes
Strength Validation: Use MACD-V+ multiplier for position sizing
Extreme Respect: Both hitting extremes = high reversal probability
Methodology
MACD-V methodology is based on volatility normalization using Average True Range (ATR). This approach transforms traditional MACD into a universal momentum indicator with statistically-validated zones and objectively-defined states.
The indicator implements:
ATR-based normalization for cross-market comparability
Statistical analysis for universal zone definitions (±150, ±50)
Lifecycle state system for objective trend identification
Absolute histogram MA with direction-aware visualization (ATR-length period)
Strength multiplier: ratio of current to average absolute momentum (always positive)
Dynamic status table adapting to active trend filters
MACD-V+ transforms momentum analysis from subjective interpretation into objective, quantifiable measurements. Combined with LBR 3/10-V for tactical timing, it provides a complete framework for systematic trading across all financial markets and timeframes.
This indicator is designed for educational and analytical purposes. Past performance does not guarantee future results. Always conduct thorough research and consider consulting with financial professionals before making investment decisions.
SEVENX|SuperFundedSEVENX — Modular Multi-Signal Scanner (SuperFunded)
What it is
SEVENX combines seven classic signals—MACD, OBV, RSI, Stochastics, CCI, Momentum, and an optional ATR volatility filter—into a modular gate. You can toggle each condition on/off, and a BUY/SELL arrow prints only when all enabled conditions agree. Text labels are optional.
Why this is not a simple mashup
・Most “combo” scripts just overlay indicators. SEVENX is a strict consensus engine:
・Each condition is binary and user-switchable.
・The final signal is the logical AND of all enabled checks (no hidden weights).
・Signals fire only on confirmed events (e.g., RSI crossing a level, Stoch K/D cross), which makes entries rule-driven and reproducible.
This yields a transparent, vendor-grade workflow where traders can start simple (2–3 gates) and tighten selectivity by enabling more gates.
How it works (concise)
・MACD: macd_line > signal_line (buy) / < (sell).
・OBV trend: OBV > OBV_MA (buy) / < (sell).
・RSI bounce/drop: crossover(RSI, Oversold) (buy) / crossunder(RSI, Overbought) (sell).
・Stoch cross: %K crosses above %D (buy) / below (sell).
・CCI rebound/pullback: crossover(CCI, -Level) (buy) / crossunder(CCI, +Level) (sell).
・Momentum: Momentum > 0 (buy) / < 0 (sell).
・ATR filter (optional): ATR > ATR_MA must also be true (both sides).
・Final signal: AND of all enabled conditions. If you enable none on a side, that side will not print.
Parameters (UI mapping)
Buy Signal (group: “— Buy Signal —”)
・MACD Golden Cross / OBV Uptrend / RSI Bounce from Oversold / Stochastic Golden Cross / CCI Rebound from Oversold / Momentum > 0 / ATR Volatility Filter (on/off)
Sell Signal (group: “— Sell Signal —”)
・MACD Dead Cross / OBV Downtrend / RSI Drop from Overbought / Stochastic Dead Cross / CCI Pullback from Overbought / Momentum < 0 / ATR Volatility Filter (on/off)
Indicator Settings
・MACD: Fast/Slow/Signal lengths.
・RSI: Length, Overbought/Oversold levels.
・Stochastics: %K length, %D smoothing, overall smoothing.
・CCI: Length, Level (±Level used).
・Momentum: Length.
・OBV: MA length for trend baseline.
・ATR: ATR length, ATR MA length (for the filter).
Display
・Show Text (BUY/SELL text on the markers), Buy/Sell Text Colors.
Practical usage
・Start simple: Enable 2 conditions (e.g., MACD + RSI). If signals are too frequent, add OBV or Momentum; if still frequent, enable ATR filter.
・Mean-reversion vs trend:
・For trend-following, prefer MACD/OBV/Momentum gates.
・For reversal bounces, add RSI/CCI gates and keep Stoch for timing.
・Tuning sensitivity:
・Raise RSI Oversold/Overbought thresholds to make bounces rarer.
・Increase ATR_MA length to smooth the volatility baseline.
・Risk first: Plan SL/TP independently (e.g., structure levels or R-multiples). SEVENX focuses on entry qualification, not exits.
Repainting & confirmation
Signals depend on cross events and are best treated on bar close. Intrabar flips can occur before a bar closes; for strict rules, confirm on closed bars in your strategy.
Disclaimer
No indicator can guarantee outcomes. News, liquidity, and spread conditions can invalidate signals. Trade responsibly and manage risk.
SuperFunded invite-only
To obtain access, please DM me on TradingView or use the link in my profile.
SEVENX — モジュラー型マルチシグナル・スキャナー（日本語）
概要
SEVENXは、MACD / OBV / RSI / ストキャス / CCI / モメンタム / ATRフィルターの7条件を個別オン・オフで制御し、有効化した条件がすべて満たされたときだけBUY/SELL矢印を表示する、合意（AND）型シグナルインジです。テキスト表示も任意。
独自性・新規性
・各条件はブラックボックスではなく明示的なブール判定で、最終シグナルは有効化した条件のAND。
・RSIのレベルクロスやStochのK/Dクロスなど、確定イベントで判定するため、再現性の高いルール運用が可能。少数条件から始めて、必要に応じて段階的に厳格化できます。
動作要点
・MACD：線がシグナル上/下。
・OBV：OBVがOBVのMAより上/下。
・RSI：RSIがOSを上抜け（買い）/OBを下抜け（売り）。
・Stoch：%Kが%Dを上抜け/下抜け。
・CCI：CCIが**−Levelを上抜け**（買い）/+Levelを下抜け（売り）。
・Momentum：0より上/下。
・ATRフィルター（任意）：ATR > ATR_MA を満たすこと（買い/売り共通）。
・最終サイン：有効化した条件のAND。そのサイドで1つも有効化していなければサインは出ません。
実践ヒント
・まずは2条件（例：MACD＋RSI）でテスト → 多すぎるならOBV/MomentumやATRフィルターを追加。
・トレンド重視：MACD/OBV/Momentumを主軸に。
・押し目・戻り目狙い：RSI/CCIを追加、Stochでタイミング調整。
・感度調整：RSIのOB/OSを広げる、ATR_MAを長くする等で厳しめに。
・出口は別設計：SL/TPは価格帯やR倍数などで管理を。
再描画と確定
確定足基準で判断すると安定します。足確定前はクロスが行き来することがあります。
免責
シグナルの機能は保証されません。イベントや流動性で無効化する場合があります。資金管理のうえ自己責任でご利用ください。
SuperFunded 招待専用スクリプト
このスクリプトはSuperFundedの参加者専用です。アクセスをご希望の方は、SuperFundedにご登録のメールアドレスから partner@superfunded.com 宛に、TradingViewの登録名をご送信ください。
Trap LineTrap Line W — Weekly Trend Barrier (Closed-source)
Overview
Trap Line W is a trend-following overlay that plots a single weekly baseline to define the market’s higher-timeframe regime. Price above the line indicates a bullish regime; price below the line indicates a bearish regime. The goal is to promote regime discipline—staying aligned with the dominant direction and avoiding late, emotionally driven entries. Core parameters are fixed to ensure consistent behavior across symbols.
What it does (principles, not secrets)
• Builds a smoothed weekly baseline designed to approximate the higher-timeframe trend path.
• Uses higher-timeframe aggregation so regime assessments align with closed weekly candles.
• Acts as a simple, binary bias filter: long-only above, short/avoid longs below (framework, not advice).
Inputs
• No user-tweakable inputs. Parameters are fixed to reduce overfitting and improve repeatability.
How to read it
• Above the line ⇒ bullish regime.
• Below the line ⇒ bearish regime.
• A confirmed weekly close through the line suggests a potential regime transition; intrawEEK moves may fade.
Practical use cases
• Bias gating: enable/disable long or short playbooks based on the weekly regime.
• Portfolio overlay: apply to a watchlist; prefer allocations aligned with the weekly regime.
• Risk context: in a bullish regime, tolerate pullbacks selectively; in a bearish regime, be conservative with counter-trend exposure.
• Timeframe bridging: weekly sets bias; lower timeframes handle entries/exits.
Best practices
• Wait for the weekly close before declaring a regime flip.
• Combine with market structure (HH/HL vs. LH/LL), volume behavior, and higher-timeframe S/R.
• Prefer time-based candles and liquid instruments for clearer behavior.
Charting & data notes
• Values derive from the weekly timeframe and finalize on the weekly close; interim values may update during formation.
• Use standard time-based candles. Avoid interpreting signals on Heikin Ashi, Renko, Kagi, Point & Figure, or Range charts.
Common pitfalls
• Front-running the weekly close can cause false regime flips.
• Overtrading counter-trend near the line often has lower expectancy.
• Ignoring liquidity/news risk can lead to whipsaws around the baseline.
Who it’s for
• Swing and position traders needing a clear, rules-based regime filter.
• Systematic traders who prefer a simple, fixed-parameter bias overlay.
Limitations & disclosures
• Closed-source; for educational and analytical use only.
• Not financial advice. Markets involve risk; past performance is not indicative of future results.
Suggested screenshot captions
• “Bullish regime: weekly close above Trap Line W; pullbacks respecting the line.”
• “Bearish regime: weekly close below Trap Line W; rallies capped near the line.”
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
…
The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
...
Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞
RVol+ Enhanced Relative Volume Indicator📊 RVol+ Enhanced Relative Volume Indicator
Overview
RVol+ (Relative Volume Plus) is an advanced time-based relative volume indicator designed specifically for swing traders and breakout detection. Unlike simple volume comparisons, RVol+ analyzes volume at the same time of day across multiple sessions, providing statistically significant insights into institutional activity and breakout potential.
🎯 Key Features
Core Volume Analysis
Time-Based RVol Calculation - Compares current cumulative volume to the average volume at this exact time over the past N days
Statistical Z-Score - Measures volume in standard deviations from the mean for true anomaly detection
Volume Percentile - Shows where current volume ranks historically (0-100%)
Sustained Volume Filter - 3-bar moving average prevents false signals from single-bar spikes
Breakout Detection
🚀 Confirmed Breakouts - Identifies price breakouts validated by high volume (RVol > 1.5x)
⚠️ False Breakout Warnings - Alerts when price breaks key levels on low volume (high failure risk)
Multi-Timeframe Context - Weekly volume overlay prevents chasing daily noise
Advanced Metrics
OBV Divergence Detection - Spots bullish/bearish accumulation/distribution patterns
Volume Profile Integration - Identifies institutional positioning
Money Flow Analysis - Tracks smart money vs retail activity
Extreme Volume Alerts - 🔥 Labels mark unusual spikes beyond the display cap
Visual Intelligence
Smart Color Coding:
🟢 Bright Teal = High activity (RVol ≥ 1.5x)
🟡 Medium Teal = Caution zone (RVol ≥ 1.2x)
⚪ Light Teal = Normal activity
🟠 Orange = Breakout confirmed
🔴 Red = False breakout risk
Comprehensive Stats Table:
Current Volume (formatted as M/K/B)
RVol ratio
Z-Score with significance
Volume percentile
Historical average and standard deviation
Sustained volume confirmation
📈 How to Use
For Swing Trading (1D - 3W Holds)
Perfect Setup:
✓ RVol > 1.5x (bright teal)
✓ Z-Score > 2.0 (⚡ alert)
✓ Percentile > 90%
✓ Sustained = ✓
✓ 🚀 Breakout label appears
Avoid:
✗ Red "Low Vol" warning during breakouts
✗ RVol < 1.0 at key levels
✗ Sustained volume not confirmed
Signal Interpretation
⚡ Z>2 Labels - Statistically significant volume (95th+ percentile) - highest probability moves
↗️ OBV+ Labels - Bullish accumulation (OBV rising while price consolidates)
↘️ OBV- Labels - Bearish distribution (OBV falling while price rises)
🔵 Blue Background - Weekly volume elevated (confirms daily strength)
⚙️ Customization
Basic Settings
N Day Average - Number of historical days for comparison (default: 5)
RVol Thresholds - Customize highlight levels (default: 1.2x, 1.5x)
Visual Display Cap - Prevent extreme spikes from compressing view (default: 4.0x)
Advanced Metrics (Toggle On/Off)
Z-Score analysis
Weekly RVol context
OBV divergence detection
Volume percentile ranking
Breakout signal generation
Table Customization
Position - 9 placement options to avoid chart overlap
Size - Tiny to Huge
Colors - Full customization of positive/negative/neutral values
Transparency - Adjustable background
Debug Mode
Enable Pine Logs for calculation transparency
Adjustable log frequency
Real-time calculation breakdown
🔬 Technical Details
Algorithm:
Binary search for historical lookups (O(log n) performance)
Time-zone aware session detection
DST-safe timestamp calculations
Exponentially weighted standard deviation
Anti-repainting architecture
Performance:
Optimized for max_bars_back = 5000
Efficient array management
Built-in function optimization
Memory-conscious data structures
📊 What Makes RVol+ Different?
vs. Standard Volume:
Context-aware (time-of-day matters)
Statistical significance testing
False breakout filtering
vs. Basic RVol:
Z-Score normalization (2-3 sigma detection)
Multi-timeframe confirmation
OBV divergence integration
Sustained volume filtering
Smart visual scaling
vs. Professional Tools:
Free and open-source
Fully customizable
No black-box algorithms
Educational debug logs
💡 Best Practices
Wait for Confirmation - Don't enter on first bar; wait for sustained volume ✓
Combine with Price Action - RVol validates, price structure determines entry
Weekly Context Matters - Blue background = institutional interest
Z-Score is King - Focus on ⚡ alerts for highest probability
Avoid Low Volume Breakouts - Red ⚠️ labels = high failure risk
🎓 Trading Psychology
Volume precedes price. When RVol+ shows:
High RVol + Rising OBV = Accumulation before breakout
High RVol at Resistance = Test of conviction
Low RVol on Breakout = Retail-driven (fade candidate)
Z-Score > 3 = Potential "whale" positioning
📝 Credits
Based on the time-based RVol concept from /u/HurlTeaInTheSea, enhanced with:
Statistical analysis (z-scores, percentiles)
Multi-timeframe integration
OBV divergence detection
Professional-grade visualization
Swing trading optimization
🔧 Version History
v2.0 - Enhanced Edition
Added Z-Score analysis
Multi-timeframe volume context
OBV divergence detection
Breakout confirmation system
Smart color coding
Customizable stats table
Debug logging mode
Performance optimizations
📚 Learn More
For optimal use with swing trading:
Combine with support/resistance levels
Watch for volume clusters in consolidation
Use weekly timeframe for trend confirmation
Monitor OBV divergence for early warnings
⚠️ Disclaimer
This indicator is for educational purposes. Volume analysis is one component of trading decisions. Always use proper risk management, consider multiple timeframes, and validate signals with price structure. Past performance does not guarantee future results.
🚀 Getting Started
Add indicator to chart
Adjust "N Day Average" to your preference (5-10 days typical)
Position stats table to avoid overlap
Enable features you want to monitor
Watch for 🚀 breakout confirmations!
Happy Trading! 📈
Ober Trend Oscillator [by Oberlunar]The Ober Trend Oscillator by Oberlunar unifies a volume-weighted view of price with order-flow information in a single, disciplined signal. At its core is a Triple Hull Moving Average applied to the session VWAP. This pairing is intentional: the Hull family is widely used because its quadratic weighting and internal differencing reduce phase lag versus SMA/EMA while preserving a smooth, readable contour; running it on top of VWAP anchors the calculation to a price already “risk-weighted” by volume, which behaves in practice like a microstructural equilibrium level. Around VWAP, the indicator computes standard-deviation envelopes that provide statistical context; excursions to the far band against the prevailing direction often mark probabilistic excess and become the first checkpoint for signal qualification.
The order-flow module is built on a tick-rule Cumulative Volume Delta, the most robust choice when native bid/ask deltas are unavailable. Volumes are signed by up- or down-moves, cumulatively integrated, then smoothed by a configurable EMA. To make the series comparable across instruments and timeframes, the CVD is standardised via an adjustable z-score window. This normalisation matters because it reframes “push” and “exhaustion” as deviations from recent behaviour rather than absolute thresholds tied to each market’s idiosyncratic liquidity. When enabled, a pivot-based divergence engine searches for fresh local highs or lows in price that the CVD refuses to confirm and annotates the symbol Δ with the percentage size of the divergence on price, on CVD, or both. Quantifying divergence avoids binary, eye-ball readings and lets you compare the relative strength of signals over time.
Signal generation follows a two-stage logic. Stage one is regime detection by the THMA on VWAP. The slope of the long THMA defines the primary trend, while the instantaneous difference between the THMA and its own lag sets the “serpentine” colour that conveys the local direction of pressure. Using slope on the longer window is deliberate: trend-following practice shows that slope filters materially reduce false positives in choppy regimes. Stage two enforces contextual alignment between price and higher-timeframe VWAP bands. For a long, the THMA computed on the higher-timeframe VWAP must sit below the current curve and below the second lower deviation, consistent with either a mean-reverting excess or early re-accumulation; shorts are defined symmetrically. Volume-flow confirmation is then required through either a rising CVD, a supportive z-score, or a detected pivot divergence in the same direction. To discourage over-trading, signals alternate by design and a strict colour gate is applied: a green diamond is never printed on a red line and bullish divergences are not drawn when the serpentine indicates bearish pressure. This visual consistency is not cosmetic; it reduces cognitive dissonance between filters and execution signal and improves reading discipline.
Parameters are organised to make these choices explicit. The main THMA length controls the oscillator’s sensitivity to VWAP, while the “trend” and “long-term” lengths drive the slope filter, with the latter acting as the regime anchor. The higher timeframe used to compute THMA on VWAP is the context-alignment knob and enables true multi-period operation, which is essential in fractal markets such as crypto, FX and equity indices. The VWAP deviation multiplier sets the breadth of the statistical bands; values modestly below one are a deliberate default to keep excess detection sensitive without turning the envelopes into a very wide channel. The ATR window that drives the line’s thickness is not a visual gimmick: thickness adapts to volatility and communicates the movement’s energy at a glance, much like an adaptive envelope.
The CVD package offers full control. A dedicated timeframe lets you decouple order-flow estimation from the chart’s timeframe when a slower, more reliable read of pressure is preferred. The calculation mode can reference Close-to-Close for responsiveness or HL2 for slightly greater robustness to closing noise, depending on the instrument’s microstructure. EMA smoothing governs granularity, the slope lookback sets how many observations are required to validate an inflection, and the z-score length defines the statistical horizon for normalisation—longer windows make the signal steadier, shorter windows make it more tactical. The pivot divergence option with percentage sizing grades relevance rather than merely flagging presence. Measuring both the price change between pivots and the CVD change is intentional: the most actionable divergences exhibit not only directionally opposing shapes but also a quantitative mismatch between price and flow; putting the two numbers side by side clarifies whether price is outrunning flow or flow is reversing ahead of price.
On the attached weekly Bitcoin example, the turquoise serpentine highlights impulsive phases while red denotes retracement or distribution. Δ labels with “P:%” and “C:%” mark points where price sets a new extreme without a matching CVD extreme; the percentage annotation helps distinguish a trivial imbalance from a credible exhaustion. Diamonds appear only when their colour agrees with the serpentine, and their location relative to the higher-TF VWAP bands clarifies when the market stops pushing “with volume” and starts pushing “against volume”—often the operational cue that precedes mean reversion or a consolidation before the next impulse.
Three methodological choices deserve emphasis. The THMA-on-VWAP architecture addresses the classic lag-versus-noise trade-off by combining a low-lag smoother with a volume-anchored base series that reflects institutional execution practice. Z-scoring the CVD is consistent with a statistical reading of flow that reasons in deviations from expected behaviour rather than fixed thresholds, which is particularly relevant on assets with shifting liquidity regimes. Finally, the colour gate plus signal alternation mitigates the well-known clustering of false positives in sideways markets: you do not print green on red or red on green, and you do not fire the same direction twice in a row without an opposite transition, which avoids hammering into the same move.
Practical usage is straightforward. Select your trading timeframe and align context with a higher timeframe in the VWAP-THMA; tune the VWAP deviation multiplier to match the instrument’s excess profile; choose an equal or slower CVD timeframe to extract structural pressure; enable divergence sizing when you want to measure, not only see, the gap between price and flow. Signals can also be drawn on the main chart, so next to candles, you will see both the execution diamonds and Δ labels with their percentage sizes. If you work with higher-timeframe inputs via `request.security`, be aware that those series confirm only at their own close; you can require confirmation for both the higher-TF VWAP and CVD timeframes to eliminate any practical repaint. Integrated alerts tied to THMA+VWAP+CVD validation convert discretionary reading into a monitorable workflow consistent with systematic routines.
Known limitations are stated explicitly. Tick-rule CVD is an approximation and, while standard in the absence of native bid/ask deltas, it may diverge from “true” delta on venues with unusual execution dynamics; normalisation helps but does not eliminate this. Pivot divergences depend on swing definition and require sensitivity calibration to avoid over-signalling on erratic markets. By construction, the oscillator favours trending contexts with statistically motivated pullbacks; during prolonged congestion, signals will naturally thin out, and the standardised CVD becomes the primary discriminator.
In sum, the Ober Trend Oscillator is a dual-channel reader: the THMA-on-VWAP line tells you about regime and movement quality, and the normalised CVD tells you about the pressure sustaining that movement. When the two stories align, continuation probability improves; when they diverge, the Δ annotation quantifies the gap and offers an objective basis for judging whether you are seeing a healthy pause or an impending reversal. The integration of volume-weighted price, simple statistics, and order-flow makes the indicator genuinely multi-period, capable of scaling from intraday to swing without changing its visual language or its decision criteria.
Oberlunar 👁️⭐
15m Continuation — prev → new (v6, styled)This indicator gives you backtested statistics on how often reversals vs continuations occur on 15 minute candles on any pair you want to trade. This is great for 15m binary markets like on Polymarket.
RSI+VOL——Binary（One bar）Overview
This indicator integrates Stochastic RSI, MACD trend alignment, ADX trend strength, and multi-dimensional volume analysis to provide intelligent signal guidance and market activity monitoring. It is suitable for short-term, swing, and event-driven trading, offering clear visualization of trend direction, market strength, and volume anomalies.
Core Features
1️⃣ Stochastic RSI Signals
Automatically identifies overbought and oversold conditions to generate buy and sell reference signals.
Signals are filtered with candle closing direction to reduce counter-trend entries.
2️⃣ MACD Trend Alignment
Signals trigger only when MACD trend direction aligns with Stochastic RSI, improving accuracy.
Real-time trend alignment reduces noise from ranging markets.
3️⃣ ADX Trend Strength Filter
Signals trigger only when ADX indicates a significant trend, filtering out low-strength movements.
Helps capture primary market directions.
4️⃣ Multi-Dimensional Volume Analysis
Differentiates bullish and bearish volume to identify breakout signals.
Relative volume (RVOL) ensures signals occur during periods of active trading.
Background highlights abnormal spikes and extreme volume, clearly reflecting market activity.
5️⃣ Signal Visualization and Alerts
Buy and sell labels with corresponding RSI values are displayed on the chart.
Built-in alert conditions support TradingView notifications and strategy integration.
Indicator Value
Multi-dimensional alignment: combines trend, momentum, and market activity for comprehensive assessment.
High-precision signal reference: filters noise and provides clear entry indications.
Market activity monitoring: highlights extreme volume to reflect market participation.
Broad applicability: suitable for short-term, swing, and event-driven trading across various markets.
[DEM] Parabolic SAR Bars (PSAR Bars) Parabolic SAR Bars is a visual enhancement of the traditional Parabolic SAR indicator that uses dynamic color coding to represent the relative position and momentum of price versus the SAR levels. The indicator calculates the percentage difference between the closing price and the Parabolic SAR value, then applies either a gradient color scheme that transitions from red to blue based on the relative strength within a 20-period range, or a momentum-based coloring system using purple, blue, and red to indicate directional changes. Both the SAR plot points and the price bars themselves are colored according to this system, creating an intuitive visual representation where traders can quickly assess not just whether price is above or below the SAR, but also the strength and momentum of that relationship. This approach transforms the binary nature of traditional Parabolic SAR signals into a more nuanced visual tool that helps identify the intensity of trending conditions and potential momentum shifts before actual SAR reversals occur.
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
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🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
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🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
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💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
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⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
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📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
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🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
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📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
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🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
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💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
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⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
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🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
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Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
