Smart Money Flow Cloud [BOSWaves]Smart Money Flow Cloud - Volume-Weighted Trend Detection with Adaptive Volatility Bands
Overview
Smart Money Flow Cloud is a volume flow-aware trend detection system that identifies directional market regimes through money flow analysis, constructing adaptive volatility bands that expand and contract based on institutional pressure intensity.
Instead of relying on traditional moving average crossovers or fixed-width channels, trend direction, band width, and signal generation are determined through volume-weighted money flow calculation, nonlinear flow strength modulation, and volatility-adaptive band construction.
This creates dynamic trend boundaries that reflect actual institutional buying and selling pressure rather than price momentum alone - tightening during periods of weak flow conviction, expanding during strong directional moves, and incorporating flow strength statistics to reveal whether regimes formed under accumulation or distribution conditions.
Price is therefore evaluated relative to adaptive bands anchored at a flow-informed baseline rather than conventional trend-following indicators.
Conceptual Framework
Smart Money Flow Cloud is founded on the principle that sustainable trends emerge where volume-weighted money flow confirms directional price movement rather than where price alone creates patterns.
Traditional trend indicators identify regime changes through price crossovers or slope analysis, which often ignore the underlying volume dynamics that validate or contradict those movements.This framework replaces price-centric logic with flow-driven regime detection informed by actual buying and selling volume.
Three core principles guide the design:
Trend direction should correspond to volume-weighted flow dominance, not price movement alone.
Band width must adapt dynamically to current flow strength and volatility conditions.
Flow intensity context reveals whether regimes formed under conviction or uncertainty.
This shifts trend analysis from static moving averages into adaptive, flow-anchored regime boundaries.
Theoretical Foundation
The indicator combines adaptive baseline smoothing, close location value (CLV) methodology, volume-weighted flow tracking, and nonlinear strength amplification.
A smoothed trend baseline (EMA or ALMA) establishes the core directional reference, while close location value measures where price settled within each bar's range. Volume weighting applies directional magnitude to flow calculation, which accumulates into a normalized money flow ratio. Flow strength undergoes nonlinear power transformation to amplify strong conviction periods and dampen weak flow environments. Average True Range (ATR) provides volatility-responsive band sizing, with final width determined by the interaction between base volatility and flow-modulated multipliers.
Four internal systems operate in tandem:
Adaptive Baseline Engine : Computes smoothed trend reference using either EMA or ALMA methodology with configurable secondary smoothing.
Money Flow Calculation System : Measures volume-weighted directional pressure through CLV analysis and ratio normalization.
Nonlinear Flow Strength Modulation : Applies power transformation to flow intensity, creating dynamic sensitivity scaling.
Volatility-Adaptive Band Construction : Scales band width using ATR measurement combined with flow-strength multipliers that range from minimum (calm) to maximum (strong flow) expansion.
This design allows bands to reflect actual institutional behavior rather than reacting mechanically to price volatility alone.
How It Works
Smart Money Flow Cloud evaluates price through a sequence of flow-aware processes:
Close Location Value (CLV) Calculation : Each bar's closing position within its high-low range is measured, creating a directional bias indicator ranging from -1 (closed at low) to +1 (closed at high).
Volume-Weighted Flow Tracking : CLV is multiplied by bar volume, then accumulated and normalized over a configurable flow window to produce a money flow ratio between -1 and +1.
Flow Smoothing and Strength Extraction : The raw money flow ratio undergoes optional smoothing, then nonlinear power transformation to amplify strong flow periods and compress weak flow environments.
Adaptive Baseline Construction : Price (both open and close) is smoothed using either EMA or ALMA methodology with optional secondary smoothing to create a stable trend reference.
Dynamic Band Sizing : ATR measurement is multiplied by a flow-strength-modulated factor that interpolates between minimum (tight) and maximum (wide) multipliers based on current flow conviction.
Regime Detection and Visualization : Price crossing above the upper band triggers bullish regime, crossing below the lower band triggers bearish regime. The baseline cloud visualizes open-close relationship within the current trend.
Retest Signal Generation : Price touching the baseline from within an established regime generates retest signals with configurable cooldown periods to prevent noise.
Together, these elements form a continuously updating trend framework anchored in volume flow reality.
Interpretation
Smart Money Flow Cloud should be interpreted as flow-confirmed trend boundaries:
Bullish Regime (Blue) : Activated when price crosses above the upper adaptive band, indicating volume-confirmed buying pressure exceeding volatility-adjusted resistance.
Bearish Regime (Red) : Established when price crosses below the lower adaptive band, identifying volume-confirmed selling pressure breaking volatility-adjusted support.
Baseline Cloud : The gap between smoothed open and smoothed close within the baseline visualizes intrabar directional bias - wider clouds indicate stronger intrabar momentum.
Adaptive Band Width : Reflects combined volatility and flow strength - wider bands during high-conviction institutional activity, tighter bands during consolidation or weak flow periods.
Buy/Sell Labels : Appear at regime switches when price crosses from one band to the other, marking potential trend inception points.
Retest Signals (✦) : Diamond markers indicate price touching the baseline within an established regime, often occurring during healthy pullbacks in trending markets.
Trend Strength Gauge : Visual meter displays current regime strength as a percentage, calculated from price position within the active band relative to baseline.
Background Gradient : Optional coloring intensity reflects flow strength magnitude, darkening during high-conviction periods.
Flow strength, band width adaptation, and baseline relationship outweigh isolated price fluctuations.
Signal Logic & Visual Cues
Smart Money Flow Cloud presents three primary interaction signals:
Regime Switch - Buy : Blue "Buy" label appears when price crosses above the upper band after previously being in a bearish regime, suggesting volume-confirmed bullish transition.
Regime Switch - Sell : Red "Sell" label displays when price crosses below the lower band after previously being in a bullish regime, indicating volume-confirmed bearish transition.
Trend Retest : Diamond (✦) markers appear when price touches the baseline within an established regime, with configurable cooldown periods to filter noise.
Alert generation covers regime switches and retest events for systematic monitoring.
Strategy Integration
Smart Money Flow Cloud fits within volume-informed and institutional flow trading approaches:
Flow-Confirmed Entry : Use regime switches as primary trend inception signals where volume validates directional breakouts.
Retest-Based Refinement : Enter on baseline retest signals within established regimes for improved risk-reward positioning during pullbacks.
Band Width Context : Expect wider price swings when bands expand (high flow strength), tighter ranges when bands contract (weak flow).
Baseline Cloud Confirmation : Favor trades where baseline cloud width confirms intrabar momentum alignment with regime direction.
Strength Gauge Filtering : Use trend strength percentage to gauge continuation probability - higher readings suggest stronger institutional conviction.
Multi-Timeframe Regime Alignment : Apply higher-timeframe regime context to filter lower-timeframe entries, taking only setups aligned with dominant flow direction.
Technical Implementation Details
Core Engine : Configurable EMA or ALMA baseline with secondary smoothing
Flow Model : Close Location Value (CLV) with volume weighting and ratio normalization
Strength Transformation : Configurable power function for nonlinear flow amplification
Band Construction : ATR-scaled width with flow-strength-interpolated multipliers
Visualization : Dual-line baseline cloud with gradient fills, regime-colored bands, and embedded strength gauge
Signal Logic : Band crossover detection with baseline retest identification and cooldown management
Performance Profile : Optimized for real-time execution with minimal computational overhead
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-structure regime detection for scalping and intraday reversals
15 - 60 min : Intraday trend identification with flow-validated swings
4H - Daily : Swing and position-level regime analysis with institutional flow context
Suggested Baseline Configuration:
Trend Length : 34
Trend Engine : EMA
Trend Smoothing : 3
Flow Window : 24
Flow Smoothing : 5
Flow Boost : 1.2
ATR Length : 14
Band Tightness (Calm) : 0.9
Band Expansion (Strong Flow) : 2.2
Reset Cooldown : 12
These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volume profile, volatility characteristics, and preferred signal frequency, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Bands too wide/frequent whipsaws : Reduce "Band Expansion (Strong Flow)" to limit maximum band width, or increase "Band Tightness (Calm)" to widen minimum bands and reduce noise sensitivity.
Trend baseline too choppy : Increase "Trend Length" for smoother baseline, or increase "Trend Smoothing" for additional filtering.
Flow readings unstable : Increase "Flow Smoothing" to reduce bar-to-bar noise in money flow calculation.
Missing legitimate regime changes : Decrease "Trend Length" for faster baseline response, or reduce "Band Tightness (Calm)" for earlier breakout detection.
Too many retest signals : Increase "Reset Cooldown" to space out retest markers, or disable retest signals entirely if not using pullback entries.
Flow strength not responding : Increase "Flow Boost" (power factor) to amplify strong flow differentiation, or decrease "Flow Window" to emphasize recent volume activity.
Prefer different smoothing characteristics : Switch "Trend Engine" to ALMA and adjust "ALMA Offset" (higher = more recent weighting) and "ALMA Sigma" (higher = smoother) for alternative baseline behavior.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Markets with consistent volume participation and institutional flow
Instruments where volume accurately reflects true liquidity and conviction
Trending environments where flow confirms directional price movement
Mean-reversion strategies using retest signals within established regimes
Reduced Effectiveness:
Extremely low volume environments where flow calculations become unreliable
News-driven or gapped markets with discontinuous volume patterns
Highly manipulated or thinly traded instruments with erratic volume distribution
Ranging markets where price oscillates within bands without conviction
Integration Guidelines
Confluence : Combine with BOSWaves structure, order flow analysis, or traditional volume profile
Flow Validation : Trust regime switches accompanied by strong flow readings and wide band expansion
Context Awareness : Consider whether current market regime matches historical flow patterns
Retest Discipline : Use baseline retest signals as confirmation within trends, not standalone entries
Breach Management : Exit regime-aligned positions when price crosses opposing band with volume confirmation
Disclaimer
Smart Money Flow Cloud is a professional-grade volume flow and trend analysis tool. Results depend on market conditions, volume reliability, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, market context, and comprehensive risk management.
Trend
Alpha Hunter Integrated MACD & Oscillator [wjdtks255]Indicator Title: Alpha Hunter Integrated MACD & Oscillator Pro
Short Description
A high-precision hybrid oscillator that integrates MACD dynamics with a secondary-smoothed histogram to eliminate market noise and capture trend reversals with minimal lag.
Detailed Description
Overview
The Alpha Hunter Integrated MACD & Oscillator is designed to overcome the inherent lag of standard MACD indicators. By applying an exponential moving average (EMA) filter to the histogram itself and incorporating a momentum direction check, this tool identifies high-probability entry points while filtering out "whipsaws" commonly found in choppy markets.
Key Technical Pillars
Dual-Smoothed Histogram: Unlike standard oscillators, this script smooths the raw histogram values using a secondary filtering period. This reveals the true underlying momentum before price action fully shifts.
Momentum Directional Filter: Entry signals are only triggered when the MACD line’s slope aligns with the crossover, ensuring you don't enter against a stalling trend.
Dynamic Trend Clouds: The visual fill between the MACD and Signal lines acts as a "Trend Cloud," providing immediate visual feedback on the strength and duration of the current trend.
The Winning Trading Strategy (How to Use)
To maximize win rates, it is highly recommended to use this indicator as a Confirmation Oscillator alongside a Long-term Trend Filter (like a 200 EMA) on your main chart.
1. Long Setup (Buy)
Context: Price must be trading above the 200 EMA on the main chart.
Signal: A green "BUY" triangle and label appear on the oscillator.
Confirmation: The Histogram should be green and rising.
Exit: Exit at a pre-defined Take Profit (TP) box or when a bearish MACD crossunder occurs.
2. Short Setup (Sell)
Context: Price must be trading below the 200 EMA on the main chart.
Signal: A red "SELL" triangle and label appear on the oscillator.
Confirmation: The Histogram should be red and falling.
Exit: Exit at the designated Stop Loss (SL) or when a bullish MACD crossover occurs.
Input Parameters & Optimization
Fast/Slow/Signal: Default 12, 26, 9. (Standard for most markets).
Signal Smoothing: Set to 5 for a balance of speed and reliability. Increase to 8+ for swing trading on higher timeframes.
Recommended Timeframes: 15m, 1h, and 4h for the best signal-to-noise ratio.
Author's Note
This indicator is a "No-Repaint" script. Signals are confirmed at the close of the candle to ensure reliability during live trading. Always use proper risk management.
Quality-Controlled Trend StrategyOverview
This strategy demonstrates a clean, execution-aware trend framework with fully isolated risk management.
Entry conditions and risk logic are intentionally separated so risk parameters can be adjusted without altering signal behavior.
All calculations are evaluated on confirmed bars to ensure backtest behavior reflects real-time execution.
Design intent
Many scripts mix entries and exits in ways that make results fragile or misleading.
This strategy focuses on structural clarity by enforcing:
confirmed-bar logic only
fixed and transparent risk handling
consistent indicator calculations
one position at a time
It is intended as a baseline framework rather than an optimized system.
Trading logic (high level)
Trend context
EMA 50 vs EMA 200 defines directional bias
Entry
Price alignment with EMA 50
RSI used as a momentum confirmation, not as an overbought/oversold signal
Risk management
Stop-loss based on ATR
Fixed risk–reward structure
Risk logic is isolated from entry logic
Editing risk without affecting signals
All stop-loss and take-profit calculations are handled in a dedicated block.
Users can adjust:
ATR length
stop-loss multiplier
risk–reward ratio
without modifying entry conditions.
This allows controlled experimentation while preserving signal integrity.
Usage notes
Results vary by market, timeframe, and volatility conditions.
This script is provided for testing and educational purposes and should be validated across multiple symbols and forward-tested before use in live environments.
Strict EMA Wick Pullback Trend ContinuationThis script is a strict EMA pullback entry model
designed exclusively for trend continuation traders.
It does NOT attempt to predict tops or bottoms.
It waits for established trends and enters only
on shallow pullbacks with defined risk.
OVERVIEW
This strategy is built for disciplined trend continuation trading.
It looks for shallow pullbacks into a fast EMA during established uptrends
and exits when trend structure breaks.
There is no counter-trend logic and no optimization for win rate.
ENTRY LOGIC
A long entry is triggered when:
• Price pulls back into the fast EMA area (wick touch)
• The pullback remains above the slow EMA (trend integrity)
• The candle closes bullish
• Optional: slow EMA is rising (trend filter)
RISK MANAGEMENT
• A dynamic stop is placed just below the fast EMA
• The stop only tightens — it never loosens
• Losses are small and predefined
• The system is designed to be scaled via position sizing
EXIT LOGIC
• Positions are closed when the fast EMA crosses below the slow EMA
• This represents a breakdown of trend continuation structure
WHAT THIS STRATEGY IS
• A trend continuation entry module
• Risk-first by design
• Low win-rate, high payoff profile
• Designed for trending markets
WHAT THIS STRATEGY IS NOT
• Not a reversal system
• Not a scalping strategy
• Not a signal service
• Not optimized for ranging markets
• Not a promise of profitability
IMPORTANT NOTES
• Long-only by design (BTC context)
• No repainting logic
• Best used with higher-timeframe trend confirmation
• This is a tool, not financial advice
Recommended markets: BTCUSD / BTCUSDT
Timeframe: 1D
Trend filter: ON
Risk: fixed % per trade (user-defined)
NeuraCloud - Ichimoku (Purple Kumo) + Alerts (Minimal)NeuraCloud is a clean, modern interpretation of the Ichimoku Cloud, designed to identify trend direction, market structure, and key support/resistance zones at a glance.
The purple cloud (Kumo) acts as a dynamic trend filter:
• Price above the cloud indicates bullish conditions
• Price below the cloud indicates bearish conditions
• Price inside the cloud signals consolidation or uncertainty
NeuraCloud combines the cloud with Tenkan-sen and Kijun-sen to highlight momentum shifts, pullbacks, and trend continuation opportunities. Built-in alerts notify you of price/cloud breaks, momentum crosses, and cloud flips, helping you stay aligned with high-probability market structure.
Ideal for trend traders, swing traders, and multi-timeframe analysis, NeuraCloud keeps charts clean while delivering clear market context.
Market Structure Break + RSI ExitSignal Architect™ — Developer Note
This indicator includes a limited visual preview of a proprietary power signal I have personally developed and refined across futures, algorithmic systems, options, and equity trading.
Every tool I release is built with one principle in mind:
clarity of direction without over-promising or under-delivering.
That is why all Signal Architect™ tools emphasize:
Market structure first
High-probability directional context
Clear, visual risk framing
No predictive claims, no curve-fit illusions
What you are seeing here is only a small glimpse of a much broader internal framework I actively use in live environments.
🧠 Background & Scope
Over the years, I have personally developed 800+ programs spanning:
Equities
Futures
Options
Dividend & income systems
Portfolio construction and allocation logic
This includes 40+ Nasdaq-100 trading bots, several of which operate under extremely strict rule-sets and controlled deployment conditions.
Nothing shared publicly represents my full system—only educational and analytical previews designed to demonstrate how structure and probability can be aligned visually.
🤝 Support & Collaboration
If you find value in what I share:
Please subscribe, boost, and share my scripts, Ideas, and MINDS posts
You are always welcome to message me directly with questions or if you need something built or adapted
Constructive feedback and collaboration are encouraged
For traders looking to go deeper, I offer optional memberships that include:
Access to additional signals
Early previews
Occasional free tools and upgrades to support your trading journey
🔗 Membership & Signals:
trianchor.gumroad.com
⚠️ Final Note
Everything published publicly is for educational and analytical purposes only.
Markets carry risk. Discipline and risk management always come first.
— Signal Architect™
You can Find my personally developed GBT below
chatgpt.com
chatgpt.com
chatgpt.com
********************************************************************************************************************WHAT THIS INDICATOR DOES
This indicator is a structure-first breakout engine designed around how price actually transitions between balance and expansion.
It does not predict reversals.
It waits for confirmed market structure breaks, then:
Anchors risk using recent wave extremes
Projects deterministic TP/SL zones
Tracks outcomes visually and statistically
Optionally exits early when momentum exhausts (RSI fade)
This makes it ideal for:
Directional traders
Swing continuation setups
Expansion phases after compression
🧠 CORE SIGNAL ARCHITECT LOGIC
1️⃣ Market Structure Identification
The system uses pivot highs and pivot lows to define true structural levels:
Pivot High break → Long bias
Pivot Low break → Short bias
This avoids:
Random candle breakouts
Intrabar noise
False momentum spikes
Only confirmed structural levels are traded.
2️⃣ Entry Trigger (Structure Break)
A trade is triggered only when price closes through structure:
Direction Requirement
Long Close breaks above last confirmed pivot high
Short Close breaks below last confirmed pivot low
📌 Important:
No signal fires if you are already in a trade — one position at a time, clean sequencing.
3️⃣ Stop-Loss Logic (Wave-Anchored Risk)
Stops are not arbitrary.
They are anchored to:
Recent wave low (for longs)
Recent wave high (for shorts)
This ensures:
Stops sit beyond real market structure
Risk reflects actual auction failure, not candle noise
4️⃣ Take-Profit Logic (Risk × Reward)
Take-profit is mechanically derived:
TP = Risk × Risk:Reward Ratio
Examples:
RR = 1.0 → TP = same distance as SL
RR = 1.5 → TP = 1.5× SL distance
RR = 2.0 → TP = expansion-focused swings
This keeps results comparable, repeatable, and testable.
5️⃣ Optional RSI Exit (Momentum Fade)
RSI is not used for entries.
It is used only as an optional early-exit filter:
Trade RSI Condition
Long RSI crosses down from Overbought
Short RSI crosses up from Oversold
This is designed for:
Reducing give-back during exhaustion
Tight markets where expansion stalls
Volatility contraction environments
🔕 You can disable this entirely for pure structure trading.
📦 VISUAL OUTPUTS
🔲 Risk Boxes (Core Feature)
Every trade plots:
Green box = profit zone
Red box = loss zone
Boxes:
Extend forward bar-by-bar
Stop updating once trade resolves
Allow instant visual expectancy review
🔺 Signal Arrows
Green ▲ = Structure Break Long
Red ▼ = Structure Break Short
No repainting.
No intrabar guessing.
🧮 Performance Stats Table
Tracks:
Total trades
Wins
Losses
Win rate %
📌 This is contextual feedback, not a promise of future results.
🎯 RECOMMENDED TIMEFRAMES (VERY IMPORTANT)
This indicator performs best when structure matters.
⭐ PRIMARY TIMEFRAMES (Recommended)
Timeframe Use Case
15-Minute Intraday structure breaks, clean expansions
30-Minute Session-level continuation
1-Hour Swing structure, reduced noise
2-Hour Institutional rhythm, fewer false breaks
4-Hour Macro structure legs
✔ These timeframes allow pivots to form properly
✔ Stops remain structurally meaningful
✔ RR math stays realistic
⚠️ SECONDARY / CONDITIONAL
Timeframe Notes
5-Minute Use only during trend days
Daily Works well, but slower signal frequency
🚫 NOT RECOMMENDED
Timeframe Why
1–3 Minute Too much pivot distortion
Tick / Seconds Breaks structure logic entirely
This is not a scalping indicator.
🟩 BACKGROUND BIAS SHADING
Green tint → Active long bias
Red tint → Active short bias
No tint → Neutral / flat
This helps:
Avoid over-trading
Stay aligned with active structure
Recognize when the system is waiting
🧠 HOW TO USE THIS CORRECTLY
Best Practices
✔ Trade only in expansion environments
✔ Let pivots form before expecting signals
✔ Respect the stop — it is structurally valid
✔ Journal results per timeframe
Avoid
✘ Forcing trades in chop
✘ Using this as a reversal indicator
✘ Lowering timeframe to “get more signals”
⚠️ IMPORTANT DISCLAIMER
This indicator is for educational and analytical purposes only.
It does not:
Predict markets
Guarantee profits
Replace risk management
Trading involves substantial risk and can result in loss of capital.
Past performance does not guarantee future results.
Volume Channel Flow [ChartPrime]⯁ OVERVIEW — Volume Channel Flow
The Volume Channel Flow indicator dynamically tracks evolving trend channels while simultaneously analyzing volume distribution within each channel segment.
By combining adaptive volatility-based channel boundaries with real-time volume profiling, the tool highlights directional bias, structural breakouts, and zones where buy/sell pressure is concentrated.
This makes it a powerful hybrid of a trend-tracking system and a miniature volume-profile engine that updates live as the market moves.
⯁ CONCEPTS
Dynamic Volatility Channel:
Upper and lower channel levels are continuously recalculated using ATR. These levels shift only when price breaks outside the previous channel, signaling a trend transition.
Channel Segmentation:
When a channel shift occurs, the previous segment is closed and visually plotted as its own range — allowing traders to inspect each discrete “flow phase” of the market.
Embedded Volume Profile:
Inside each channel segment, the indicator builds a mini volume histogram using user-defined binning. This creates a quick visual read of how volume was distributed within that price range.
Point of Control (PoC):
The price level with the highest traded volume inside each completed segment is detected and plotted as a dashed horizontal PoC line.
Flow Bias (Bullish/Bearish):
The volume profile color adapts depending on whether cumulative delta volume (buy minus sell pressure) is positive or negative for the segment.
Breakout Labels:
When a new channel is formed, arrows mark whether the breakout occurred upward or downward.
⯁ FEATURES
Adaptive Trend Channel Construction
Channels update only when price closes beyond upper or lower volatility thresholds. This isolates trend shifts with minimal noise.
Channel Visualization Options
Choose to display full channel boxes or only trend lines using customizable styling.
Real-Time Volume Profiling
As long as the channel remains active, volume distribution is recalculated live on every bar.
PoC Projection
The PoC is drawn across the channel range, marking the highest-volume price level for each segment.
Directional Delta Coloring
Volume profiles automatically shift to bullish or bearish colors based on cumulative delta inside the channel.
Breakout Detection
Arrows highlight each transition into a new channel regime.
⯁ HOW TO USE
Spot trend changes using breakout arrows and the creation of new trend channels.
Gauge strength of a channel by examining the density and shape of the internal volume profile.
Use PoC levels as potential support/resistance interaction zones.
Validate momentum by checking whether volume delta shows bullish or bearish dominance.
Monitor channel edges to anticipate continuation or reversal setups.
⯁ CONCLUSION
The Volume Channel Flow indicator merges trend structure with volume analytics, providing a continuously adaptive picture of market flow.
It not only detects where trend phases begin and end, but also reveals what type of volume behavior shaped each segment, offering a deeper understanding of trend strength and directional pressure.
Kalman Hull Trend Score [BackQuant]Kalman Hull Trend Score
Overview
Kalman Hull Trend Score is a trend-strength and regime-evaluation indicator that combines two ideas, Kalman filtering and Hull-style smoothing, then measures persistence of that filtered trend using a rolling score. The goal is to produce a cleaner, more stable trend read than typical moving average tools, while still reacting fast enough to be practical in live markets.
Instead of treating a moving average as a simple line you cross, this indicator turns the filtered trend into an oscillator-like score that answers: “Is the smoothed trend consistently progressing, or is it stalling and degrading?”
Core idea
The indicator is built from two components:
A Kalman-based smoothing engine that estimates price state and reduces noise adaptively.
A Hull-style construction that uses multiple Kalman passes to create a responsive, low-lag trend filter.
Once the Kalman Hull filter is built, a persistence score is calculated by comparing the current Kalman Hull value to many past values. The result is a trend score that rises in sustained trends and compresses or flips during deterioration.
Why Kalman instead of standard smoothing
Traditional moving averages apply fixed smoothing rules regardless of market conditions. A Kalman filter behaves differently, it is designed to estimate an underlying state in noisy data, adjusting how much it “trusts” new price information versus prior estimates.
This script exposes that behavior through two key controls:
Measurement Noise: how noisy the observed price is assumed to be.
Process Noise: how much the underlying state is allowed to evolve from bar to bar.
Together, these settings let you tune the balance between smoothness and responsiveness without relying on blunt averaging alone.
Kalman filter mechanics (conceptual)
Each update cycle follows the classic structure:
Prediction: assume the state continues, and expand uncertainty by process noise.
Update: compute Kalman Gain, then blend the new price observation into the estimate.
Correction: reduce uncertainty based on how much the filter accepted the new information.
When measurement noise is higher, the filter becomes more conservative, smoothing harder. When process noise is higher, the filter adapts faster to regime changes, but can become more reactive.
Check out the original script:
Kalman Hull construction
The “Hull” component is not a standard HMA built from WMAs. Instead, it recreates the Hull idea using Kalman filtering as the smoothing primitive. The structure follows the same intent as HMA, reduce lag while keeping the line smooth, but does it with Kalman passes:
Apply Kalman smoothing over multiple effective lengths.
Combine them using the Hull-style weighting logic.
Run the combined output through another Kalman pass to finalize smoothing.
The result is a Kalman Hull filter that aims to track trend with less jitter than raw price, and less lag than slow averages.
Another Kalman Hull with Supertrend
Trend scoring logic
The trend score is computed by comparing the current Kalman Hull value to past Kalman Hull values over a fixed lookback range (1 to 45 bars in this script):
If current kalmanHMA > kalmanHMA , add +1
If current kalmanHMA < kalmanHMA , add -1
This produces a persistence score rather than a simple direction signal. Strong trends where the filter keeps advancing will accumulate positive comparisons. Weak trends, chop, or reversals will cause the score to flatten, decay, or flip negative.
Interpreting the score
Read the score as trend conviction and persistence:
High positive values: bullish persistence, the filtered trend is progressing consistently.
Low positive values: trend exists but is fragile, progress is slowing.
Near zero: indecision, range behavior, frequent challenges to structure.
Negative values: bearish persistence or sustained deterioration in the filtered trend.
The rate of change matters:
Score expansion suggests trend is gaining traction.
Score compression often signals consolidation or exhaustion.
Fast flips usually accompany regime transitions.
Signal thresholds and regime transitions
User-defined thresholds convert the score into regimes:
Long threshold: score must exceed this level to confirm bullish persistence.
Short threshold: a crossunder of the score triggers bearish regime transition.
This is intentionally conservative. Long bias is maintained while the score holds above the long threshold. Short transitions are event-triggered on breakdown via crossunder, helping avoid constant flipping during minor noise.
Signals are only plotted on regime changes (first bar of the flip), keeping them clean for alerts and backtests.
Visual presentation
The indicator provides multiple layers depending on how you want to use it:
Kalman Hull Trend Score oscillator, color-coded by active regime.
Optional Kalman Hull filter plotted on the price chart for structure context.
Optional threshold reference lines for quick regime mapping.
Optional candle coloring and background shading for instant readability.
You can run it as a pure score panel or as a combined panel + on-chart trend overlay.
How to use in practice
Trend filtering
Favor long setups when the score remains above the long threshold.
Reduce directional aggression when score compresses toward zero.
Treat a short-threshold breakdown as a regime risk event, not just a signal.
Trend quality assessment
Rising score supports continuation trades and adds confidence to breakouts.
Flat or falling score warns that trend persistence is fading.
If price trends but score fails to expand, trend may be weak or liquidity-driven.
Trade management
Use the Kalman Hull line as dynamic structure reference on chart.
Use score deterioration to scale out before a full regime flip.
Use regime flips as confirmation for bias shifts rather than prediction.
Tuning guidelines
Measurement Noise
Higher: smoother filter, fewer false shifts, slower to adapt.
Lower: more responsive, more sensitive to microstructure noise.
Process Noise
Higher: adapts quicker to sudden changes, but can become twitchy.
Lower: steadier state estimate, but slower during sharp regime transitions.
A practical approach is to first tune measurement noise until the Kalman Hull line matches the “clean trend structure” you want, then adjust process noise to control how quickly it reacts when the regime genuinely changes.
Summary
Kalman Hull Trend Score transforms a Kalman-based Hull-style trend filter into a quantified persistence oscillator. By combining adaptive Kalman smoothing with low-lag Hull logic and a rolling comparison score, it provides a cleaner read on trend quality than basic moving averages or single-condition trend tools. It is best used as a regime filter, trend strength gauge, and structure-aware trade management layer.
Alpha Hunter System [MTF + Risk Manager] by Amit NamdeoThis is a comprehensive, "Institutional-Grade" Trend Following System. It is designed not just to give signals, but to filter out bad trades, manage risk automatically, and help you exit at the right time.
Here is a breakdown of exactly how your script works:
1. The Core Engine (High/Low Channel)
The Foundation: Instead of a single line, the script creates a Channel using two Exponential Moving Averages (EMAs).
Green Line: 50 EMA calculated on the Highs of the candles.
Red Line: 50 EMA calculated on the Lows of the candles.
The Logic: This creates a "No-Trade Zone" in the middle.
BUY Signal: Generated when price forcefully breaks above the Green High band.
SELL Signal: Generated when price forcefully breaks below the Red Low band.
2. The "Smart Filters" (Safety Mechanisms)
To prevent false signals (whipsaws), the script checks three separate conditions before showing a Buy or Sell label:
A. Sensitivity Filter (ATR): The price must not just touch the line; it must break it by a specific margin (based on Volatility/ATR). This filters out small wicks.
B. Volume Filter: A breakout is ignored if the Volume is low. The script requires the breakout candle's volume to be higher than the recent average. This ensures "Smart Money" supports the move.
C. Sideways Filter (ADX): Uses the Average Directional Index (ADX). If the trend is weak (ADX < 20), the channel turns Gray and blocks all signals. This saves you from trading in choppy, flat markets.
D. Multi-Timeframe (MTF) Consensus: This is the "60% Rule." The script secretly checks the trend on the 5m, 15m, 1H, 4H, and Daily charts. It only allows a signal if the majority of these timeframes agree with the direction.
3. Automatic Risk Management
This feature automates the math that professional traders usually do in Excel:
Dynamic Stop Loss (SL):
On a Buy, the SL is set to the Red (Low) Band.
On a Sell, the SL is set to the Green (High) Band.
Why? This adapts the SL to the current market volatility.
Take Profit Targets (TP):
It calculates your Risk (Entry - SL).
TP1: 1.5x Risk.
TP2: 2.5x Risk.
TP3: 4.0x Risk.
Visuals: It draws these lines on the chart immediately when a signal appears, so you know exactly where to place your orders.
4. Trailing Stop (The Purple Line)
What it is: A dynamic Purple Line that appears behind the candles.
How it works: It acts like a "Chandelier Exit."
As price moves in your favor, the line moves up to lock in profit.
It never moves backward.
Exit Rule: If a candle closes across this purple line, the trend is likely over, and you should exit.
Live Label: A label follows the line showing the exact price (e.g., "Trail SL: 1950.2") so you can update your order easily.
5. Alerts & Dashboard
Dashboard (Top Right): A live status panel showing:
Current Trend (Bullish/Bearish/Sideways).
MTF Consensus (Are the other timeframes agreeing?).
Volume Status (Pass/Fail).
Alerts: You can set phone/desktop notifications for:
Entries: Buy/Sell.
Targets: TP1, TP2, TP3 Hit.
Safety: Trailing Stop Hit (Exit trade).
Summary of Visual Colors
🟢 Green Channel: Bullish Trend (Look for Buys).
🔴 Red Channel: Bearish Trend (Look for Sells).
⚪ Gray Channel: Sideways Market (Do Not Trade).
🟣 Purple Line: Your Trailing Stop Loss.
Angle-able Thick Linean options to get thicker/thick trendlines. personally i dont think 4 pixels is wide enough so i made this, here you go
Multi Cycles Slope-Fit System MLMulti Cycles Predictive System : A Slope-Adaptive Ensemble
Executive Summary:
The MCPS-Slope (Multi Cycles Slope-Fit System) represents a paradigm shift from static technical analysis to adaptive, probabilistic market modeling. Unlike traditional indicators that rely on a single algorithm with fixed settings, this system deploys a "Mixture of Experts" (MoE) ensemble comprising 13 distinct cycle and trend algorithms.
Using a Gradient-Based Memory (GBM) learning engine, the system dynamically solves the "Cycle Mode" problem by real-time weighting. It aggressively curve-fits the Slope of component cycles to the Slope of the price action, rewarding algorithms that successfully predict direction while suppressing those that fail.
This is a non-repainting, adaptive oscillator designed to identify market regimes, pinpoint high-probability reversals via OB/OS logic, and visualize the aggregate consensus of advanced signal processing mathematics.
1. The Core Philosophy: Why "Slope" Matters:
In technical analysis, most traders focus on Levels (Price is above X) or Values (RSI is at 70). However, the primary driver of price action is Momentum, which is mathematically defined as the Rate of Change, or the Slope.
This script introduces a novel approach: Slope Fitting.
Instead of asking "Is the cycle high or low?", this system asks: "Is the trajectory (Slope) of this cycle matching the trajectory of the price?"
The Dual-Functionality of the Normalized Oscillator
The final output is a normalized oscillator bounded between -1.0 and +1.0. This structure serves two critical functions simultaneously:
Directional Bias (The Slope):
When the Combined Cycle line is rising (Positive Slope), the aggregate consensus of the 13 algorithms suggests bullish momentum. When falling (Negative Slope), it suggests bearish momentum. The script measures how well these slopes correlate with price action over a rolling lookback window to assign confidence weights.
Overbought / Oversold (OB/OS) Identification:
Because the output is mathematically clipped and normalized:
Approaching +1.0 (Overbought): Indicates that the top-weighted algorithms have reached their theoretical maximum amplitude. This is a statistical extreme, often preceding a mean reversion or trend exhaustion.
Approaching -1.0 (Oversold): Indicates the aggregate cycle has reached maximum bearish extension, signaling a potential accumulation zone.
Zero Line (0.0): The equilibrium point. A cross of the Zero Line is the most traditional signal of a trend shift.
2. The "Mixture of Experts" (MoE) Architecture:
Markets are dynamic. Sometimes they trend (Trend Following works), sometimes they chop (Mean Reversion works), and sometimes they cycle cleanly (Signal Processing works). No single indicator works in all regimes.
This system solves that problem by running 13 Algorithms simultaneously and voting on the outcome.
The 13 "Experts" Inside the Code:
All algorithms have been engineered to be Non-Repainting.
Ehlers Bandpass Filter: Extracts cycle components within a specific frequency bandwidth.
Schaff Trend Cycle: A double-smoothed stochastic of the MACD, excellent for cycle turning points.
Fisher Transform: Normalizes prices into a Gaussian distribution to pinpoint turning points.
Zero-Lag EMA (ZLEMA): Reduces lag to track price changes faster than standard MAs.
Coppock Curve: A momentum indicator originally designed for long-term market bottoms.
Detrended Price Oscillator (DPO): Removes trend to isolate short-term cycles.
MESA Adaptive (Sine Wave): Uses Phase accumulation to detect cycle turns.
Goertzel Algorithm: Uses Digital Signal Processing (DSP) to detect the magnitude of specific frequencies.
Hilbert Transform: Measures the instantaneous position of the cycle.
Autocorrelation: measures the correlation of the current price series with a lagged version of itself.
SSA (Simplified): Singular Spectrum Analysis approximation (Lag-compensated, non-repainting).
Wavelet (Simplified): Decomposes price into approximation and detail coefficients.
EMD (Simplified): Empirical Mode Decomposition approximation using envelope theory.
3. The Adaptive "GBM" Learning Engine
This is the "Machine Learning" component of the script. It does not use pre-trained weights; it learns live on your chart.
How it works:
Fitting Window: On every bar, the system looks back 20 days (configurable).
Slope Correlation: It calculates the correlation between the Slope of each of the 13 algorithms and the Slope of the Price.
Directional Bonus: It checks if the algorithm is pointing in the same direction as the price.
Weight Optimization:
Algorithms that match the price direction and correlation receive a higher "Fit Score."
Algorithms that diverge from price action are penalized.
A "Softmax" style temperature function and memory decay allow the weights to shift smoothly but aggressively.
The Result: If the market enters a clean sine-wave cycle, the Ehlers and Goertzel weights will spike. If the market explodes into a linear trend, ZLEMA and Schaff will take over, suppressing the cycle indicators that would otherwise call for a premature top.
4. How to Read the Interface:
The visual interface is designed for maximum information density without clutter.
The Dashboard (Bottom Left - GBM Stats)
Combined Fit: A percentage score (0-100%). High values (>70%) mean the system is "Locked In" and tracking price accurately. Low values suggest market chaos/noise.
Entropy: A measure of disorder. High entropy means the algorithms disagree (Neutral/Chop). Low entropy means the algorithms are unanimous (Strong Trend).
Top 1 / Top 3 Weight: Shows how concentrated the decision is. If Top 1 Weight is 50%, one algorithm is dominating the decision.
The Matrix (Bottom Right - Weight Table)
This table lifts the hood on the engine.
Fit Score: How well this specific algo is performing right now.
Corr/Dir: Raw correlation and Direction Match stats.
Weight: The actual percentage influence this algorithm has on the final line.
Cycle: The current value of that specific algorithm.
Regime: Identifies if the consensus is Bullish, Bearish, or Neutral.
The Chart Overlay
The Line: The Gradient-Colored line is the Weighted Ensemble Prediction.
Green: Bullish Slope.
Red: Bearish Slope.
Triangles: Zero-Cross signals (Bullish/Bearish).
"STRONG" Labels: Appears when the cycle sustains a value above +0.5 or below -0.5, indicating strong momentum.
Background Color: Changes subtly to reflect the aggregate Regime (Strong Up, Bullish, Neutral, Bearish, Strong Down).
5. Trading Strategies:
A. The Slope Reversal (OB/OS Fade)
Concept: Catching tops and bottoms using the -1/+1 normalization.
Signal: Wait for the Combined Cycle to reach extreme values (>0.8 or <-0.8).
Trigger: The entry is taken not when it hits the level, but when the Slope flips.
Short: Cycle hits +0.9, color turns from Green to Red (Slope becomes negative).
Long: Cycle hits -0.9, color turns from Red to Green (Slope becomes positive).
B. The Zero-Line Trend Join
Concept: Joining an established trend after a correction.
Signal: Price is trending, but the Cycle pulls back to the Zero line.
Trigger: A "Triangle" signal appears as the cycle crosses Zero in the direction of the higher timeframe trend.
C. Divergence Analysis
Concept: Using the "Fit Score" to identify weak moves.
Signal: Price makes a Higher High, but the Combined Cycle makes a Lower High.
Confirmation: Check the GBM Stats table. If "Combined Fit" is dropping while price is rising, the trend is decoupling from the cycle logic. This is a high-probability reversal warning.
6. Technical Configuration:
Fitting Window (Default: 20): The number of bars the ML engine looks back to judge algorithm performance. Lower (10-15) for scalping/quick adaptation. Higher (30-50) for swing trading and stability.
GBM Learning Rate (Default: 0.25): Controls how fast weights change.
High (>0.3): The system reacts instantly to new behaviors but may be "jumpy."
Low (<0.15): The system is very smooth but may lag in regime changes.
Max Single Weight (Default: 0.55): Prevents one single algorithm from completely hijacking the system, ensuring an ensemble effect remains.
Slope Lookback: The period over which the slope (velocity) is calculated.
7. Disclaimer & Notes:
Repainting: This indicator utilizes closed bar data for calculations and employs non-repainting approximations of SSA, EMD, and Wavelets. It does not repaint historical signals.
Calculations: The "ML" label refers to the adaptive weighting algorithm (Gradient-based optimization), not a neural network black box.
Risk: No indicator guarantees future performance. The "Fit Score" is a backward-looking metric of recent performance; market regimes can shift instantly. Always use proper risk management.
Author's Note
The MCPS-Slope was built to solve the frustration of "indicator shopping." Instead of switching between an RSI, a MACD, and a Stochastic depending on the day, this system mathematically determines which one is working best right now and presents you with a single, synthesized data stream.
If you find this tool useful, please leave a Boost and a Comment below!
Multi Asset & Multi Timeframe Trend DashboardOverview
The Multi-Asset & Multi-Timeframe Trend Dashboard is a comprehensive visual data terminal designed to provide a bird's-eye view of market sentiment across five different assets and seven distinct timeframes simultaneously. By consolidating 10 core technical indicators into a single table, it eliminates the need for "chart hopping" and helps traders identify high-probability trend alignment.
How It Works
The dashboard evaluates each asset based on a Scoring System ($-10$ to $+10$). For every timeframe, the script analyzes the following 10 conditions:
Trend: EMA 20 > EMA 50Macro
Trend: EMA 50 > EMA 200
Position: Price > EMA 200
MACD: MACD Line > Signal Line
MACD Momentum: MACD Histogram > 0
RSI Momentum: RSI(14) > RSI SMA(14)
RSI Level: RSI(14) > 50
Stochastics: Stoch K > D
CCI: Commodity Channel Index > 0
Awesome Oscillator: AO > 0
Visual Logic & Features
Indicator Dots (■): Represent the 10 individual technical conditions. Green indicates a bullish state; Red indicates a bearish state.
Trend Arrows (▲/▼): Displays the aggregate directional bias of a timeframe based on the sum of the 10 dots.
Neutral State (✖): If indicators are split 50/50 (Score of 0), a grey cross is displayed to indicate total market indecision.
"ALL" Column: A macro-summary that aggregates scores across all four primary timeframes.
Volatility Marker (•): A dot appearing next to the symbol name indicates that current ATR is higher than the historical average (user-defined threshold).
Market Status Color: The symbol name background turns Green if the market is currently open and active, and Red if it is closed or stagnant.
Technical Implementation
This script utilizes request.security calls to fetch data across timeframes. To ensure performance and prevent repainting issues, all security calls are handled using the barstate.islast flag to only render the dashboard on the most recent bar.
How to Use
Alignment Trading: Look for "Full House" scenarios where all arrows (15m through Daily) are the same color.
Scalping Bias: Use the "Mini Timeframes" (1m, 3m, 5m) to find entries that align with the higher timeframe trend shown in the main table.
Volatility Filter: Only take trades when the volatility marker (•) is active to ensure there is enough "power" in the move.
LogTrend Retest EngineLogTrend Retest Engine (LTRE)
LogTrend Retest Engine (LTRE) is an advanced trend-continuation overlay designed to identify high-probability breakout retests using logarithmic regression , volatility-adjusted deviation bands , and market regime filtering .
Unlike traditional channels or moving averages, LTRE models price behavior in log space , allowing it to adapt naturally to exponential market moves common in crypto, indices, and long-term trends.
🔹 How It Works
Logarithmic Regression Core
Performs linear regression on log-transformed price and time
Produces a structurally accurate trend midline that scales with price growth
Volatility-Adjusted Deviation Bands
Dynamic upper and lower zones based on statistical deviation
ATR weighting expands or contracts bands as volatility changes
Adaptive Lookback (Optional)
Automatically adjusts regression length using volatility pressure
Faster response in high-volatility environments, smoother in consolidation
🔹 Market Regime Detection
LTRE actively filters conditions using:
R² trend strength (trend quality, not just slope)
Volatility compression vs expansion
User-defined minimum trend strength threshold
Signals are disabled during ranging or low-quality conditions .
🔹 Breakout → Retest Signal Logic
LTRE does not chase breakouts.
Signals trigger only when:
1. Price breaks cleanly outside the deviation band
2. Market regime is confirmed as trending
3. Price performs a controlled retest within a user-defined tolerance
BUY
Break above upper band → retest → trend confirmed
SELL
Break below lower band → retest → trend confirmed
This structure is designed to reduce false breakouts and late entries.
🔹 Visual & Projection Tools
Clean midline and deviation bands
Optional filled zones
Optional future trend projection for forward structure planning
On-chart statistics for trend strength and volatility compression
🔹 Best Use Cases
Trend continuation & pullback strategies
Crypto, Forex, Indices, and equities
Works best on 15m and higher timeframes
⚠️ Disclaimer
LTRE is a decision-support tool , not a complete trading system. Always use proper risk management and confirm signals with additional structure, volume, or higher-timeframe context.
Built for traders who wait for structure — not noise.
Adaptive Log Trend Zones + Retest SignalsAdaptive Log Trend Zones + Retest Signals
Adaptive Log Trend Zones is a trend-following overlay built to identify high-probability breakout retests in strong market conditions. It combines logarithmic regression , volatility-adaptive behavior , and ATR-based trend zones to help traders stay aligned with dominant momentum while avoiding chop.
🔹 Core Features
Logarithmic Regression Midline
Uses linear regression on log price to better handle exponential market moves
Produces smoother, more realistic trend structure on higher timeframes
Volatility-Adaptive Lookback
Automatically expands or contracts the regression length based on ATR volatility
Reacts faster in high volatility, smoother in consolidation
Dynamic Trend Zones
Upper and lower bands are ATR-adjusted and trend-colored
Optional future projection for visual trend guidance
Breakout → Retest Signal Logic
Detects clean breakouts beyond the trend zone
Waits for a controlled pullback (retest) before signaling
Signals only trigger when trend strength is confirmed
Trend Quality Filter
Internal regime detection filters out low-quality, sideways conditions
Uses slope strength and volatility compression to validate entries
🔹 Signals
BUY : Bullish breakout followed by a valid retest in a trending regime
SELL : Bearish breakout followed by a valid retest in a trending regime
Signals are designed for trend continuation , not mean reversion.
🔹 Best Use Cases
Crypto, Forex, and Index markets
Higher timeframes (15m+ recommended)
Trend continuation and pullback strategies
⚠️ Notes
This indicator is not a standalone trading system . Always use proper risk management and confirm signals with structure, volume, or higher-timeframe context.
Designed for traders who prefer structure, patience, and momentum alignment.
Cumulative Volume Delta (CVD) Suite [QuantAlgo]🟢 Overview
The Cumulative Volume Delta (CVD) Suite is a comprehensive toolkit that tracks the net difference between buying and selling pressure over time, helping traders identify significant accumulation/distribution patterns, spot divergences with price action, and confirm trend strength. By visualizing the running balance of volume flow, this indicator reveals underlying market sentiment that often precedes significant price movements.
🟢 How It Works
The indicator begins by determining the optimal timeframe for delta calculation. When auto-select is enabled, it automatically chooses a lower timeframe based on your chart period, e.g., using 1-second bars for minute charts, 5-second bars for 5-minute charts, and progressively larger intervals for higher timeframes. This granular approach captures volume flow dynamics that might be missed at the chart level.
Once the timeframe is established, the indicator calculates volume delta for each bar using directional classification:
getDelta() =>
close > open ? volume : close < open ? -volume : 0
When a bar closes higher than it opens (bullish candle), the entire volume is counted as positive delta representing buying pressure. Conversely, when a bar closes lower than its open (bearish candle), volume becomes negative delta representing selling pressure. This classification is applied to every bar in the selected lower timeframe, then aggregated upward to construct the delta for each chart bar:
array deltaValues = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, getDelta())
float barDelta = 0.0
if array.size(deltaValues) > 0
for i = 0 to array.size(deltaValues) - 1
barDelta := barDelta + array.get(deltaValues, i)
This aggregation process sums all the individual delta values from the lower timeframe bars that comprise each chart bar, capturing the complete volume flow activity within that period. The resulting bar delta then feeds into the various display calculations:
rawCVD = ta.cum(barDelta) // Cumulative sum from chart start
smoothCVD = ta.sma(rawCVD, smoothingLength) // Smoothed for noise reduction
rollingCVD = math.sum(barDelta, rollingLength) // Rolling window calculation
Note: This directional bar approach differs from exchange-level orderflow CVD, which uses tick data to separate aggressive buy orders (executed at the ask price) from aggressive sell orders (executed at the bid price). While this method provides a volume flow approximation rather than pure tape-reading precision, it offers a practical and accessible way to analyze buying and selling dynamics across all timeframes and instruments without requiring specialized data feeds on TradingView.
🟢 Key Features
The indicator offers five distinct visualization modes, each designed to reveal different aspects of volume flow dynamics and cater to various trading strategies and market conditions.
1. Oscillator (Raw): Displays the true cumulative volume delta from the beginning of chart history, accompanied by an EMA signal line that helps identify trend direction and momentum shifts. When CVD crosses above the signal line, it indicates strengthening buying pressure; crosses below suggest increasing selling pressure. This mode is particularly valuable for spotting long-term accumulation/distribution phases and identifying divergences where CVD makes new highs/lows while price fails to confirm, often signaling potential reversals.
2. Oscillator (Smooth): Applies a simple moving average to the raw CVD to filter out noise while preserving the underlying trend structure, creating smoother signal line crossovers. Use this when trading trending instruments where you need confirmation of genuine volume-backed moves versus temporary volatility spikes.
3. Oscillator (Rolling): Calculates cumulative delta over only the most recent N bars (configurable window length), effectively resetting the baseline and removing the influence of distant historical data. This approach focuses exclusively on current market dynamics, making it highly responsive to recent shifts in volume pressure and particularly useful in markets that have undergone regime changes or structural shifts. This mode can be beneficial for traders when they want to analyze "what's happening now" without legacy bias from months or years of prior data affecting the readings.
4. Histogram: Renders the per-bar volume delta as individual histogram bars rather than cumulative values, showing the immediate buying or selling pressure that occurred during each specific candle. Positive (green) bars indicate that bar closed higher than it opened with buying volume, while negative (red) bars show selling volume dominance. This mode excels at identifying sudden volume surges, exhaustion points where large delta bars fail to move price, and bar-by-bar absorption patterns where one side is aggressively consuming the other's volume.
5. Candles: Transforms CVD data into OHLC candlestick format, where each candle's open represents the CVD at the start of the bar and subsequent intra-bar delta changes create the high, low, and close values. This visualization reveals the internal volume flow dynamics within each time period, showing whether buying or selling pressure dominated throughout the bar's formation and exposing intra-bar reversals or sustained directional pressure. Use candle wicks and bodies to identify volume acceptance/rejection at specific CVD levels, similar to how price candles show acceptance/rejection at price levels.
▶ Built-in Alert System: Comprehensive alerts for all display modes including bullish/bearish momentum shifts (CVD crossing signal line), buying/selling pressure detection (histogram mode), and bullish/bearish CVD candle formations. Fully customizable with exchange and timeframe placeholders.
▶ Visual Customization: Choose from 5 color presets (Classic, Aqua, Cosmic, Ember, Neon) or create your own custom color schemes. Optional price bar coloring feature overlays CVD trend colors directly onto your main chart candles, providing instant visual confirmation of volume flow and making divergences immediately apparent. Optional info label with configurable position and size displays current CVD values, data source timeframe, and mode at a glance.
EMA + PDH/PDL 2 Days [Scalping-Algo]🎯 Overview
A clean, focused scalping indicator designed for 2-minute and 4-minute stock charts. Combines trend-following EMAs with key daily support/resistance zones to identify high-probability scalp entries.
🛠️ What's Included
ComponentDescription🟡 EMA 13Fast momentum line🟣 EMA 48Medium trend filter🔴 EMA 200Major trend direction🔵 PDH/PDLPrevious day high & low zones🟠 PDH-2/PDL-22 days ago high & low zones
⏰ Session Filter
Only displays levels during regular trading hours (9:30 AM - 4:00 PM EST) to keep your chart clean during pre/post market.
📊 How to Use for Scalping
✅ Long Setup (2m/4m chart)
Price above EMA 200 (bullish bias)
Price pulls back to PDH/PDL zone or EMA 48
EMA 13 crosses above EMA 48
Enter on bounce from zone
Target: next resistance zone or 1:2 R/R
❌ Short Setup (2m/4m chart)
Price below EMA 200 (bearish bias)
Price rallies into PDH/PDL zone or EMA 48
EMA 13 crosses below EMA 48
Enter on rejection from zone
Target: next support zone or 1:2 R/R
💡 Pro Tips
TipWhy🔥 Trade the first hourMost volume & volatility🎯 Zone confluenceBest setups when PDH/PDL aligns with EMAs⚡ Quick exitsScalping = small gains, don't overstay🚫 Avoid chopSkip trades when price is stuck between zones📉 Respect EMA 200Don't long below it, don't short above it
🔵 Zone Colors Explained
Blue zones → Yesterday's high/low (stronger levels)
Orange zones → 2 days ago high/low (secondary levels)
Zone thickness → 20 ticks buffer for natural price noise
⚙️ Best Settings
TimeframeBest For2 minuteQuick scalps, 5-15 cent targets4 minuteSlightly larger moves, less noise
📌 Recommended Pairs
Works best on liquid stocks with tight spreads:
SPY, QQQ, AAPL, TSLA, AMD, NVDA, META, AMZN
⚠️ Risk Management
RuleSuggestion🛑 Stop lossBelow/above the zone (tight)🎯 Take profit1:2 or 1:3 risk/reward minimum📏 Position sizeMax 1-2% account risk per trade
🚀 Quick Start
Add indicator to 2m or 4m chart
Wait for price to reach a colored zone
Confirm trend direction with EMA 200
Look for EMA 13/48 alignment
Enter with tight stop, scale out at targets
EDUVEST Lorentzian ClassificationEDUVEST Lorentzian Classification - Machine Learning Signal Detection
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█ ORIGINALITY
This indicator enhances the original Lorentzian Classification concept by jdehorty with EduVest's visual modifications and alert system integration. The core innovation is using Lorentzian distance instead of Euclidean distance for k-NN classification, providing more robust pattern recognition in financial markets.
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█ WHAT IT DOES
- Generates BUY/SELL signals using machine learning classification
- Displays kernel regression estimate for trend visualization
- Shows prediction values on each bar
- Provides trade statistics (Win Rate, W/L Ratio)
- Includes multiple filter options (Volatility, Regime, ADX, EMA, SMA)
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█ HOW IT WORKS
【Lorentzian Distance Calculation】
Unlike Euclidean distance, Lorentzian distance uses logarithmic transformation:
d = Σ log(1 + |xi - yi|)
This provides:
- Better handling of outliers
- More stable distance measurements
- Reduced sensitivity to extreme values
【Feature Engineering】
The classifier uses up to 5 configurable features:
- RSI (Relative Strength Index)
- WT (WaveTrend)
- CCI (Commodity Channel Index)
- ADX (Average Directional Index)
Each feature is normalized using the n_rsi, n_wt, n_cci, or n_adx functions.
【k-Nearest Neighbors Classification】
1. Calculate Lorentzian distance between current bar and historical bars
2. Find k nearest neighbors (default: 8)
3. Sum predictions from neighbors
4. Generate signal based on prediction sum (>0 = Long, <0 = Short)
【Kernel Regression】
Uses Rational Quadratic kernel for smooth trend estimation:
- Lookback Window: 8
- Relative Weighting: 8
- Regression Level: 25
【Filters】
- Volatility Filter: Filters signals during extreme volatility
- Regime Filter: Identifies market regime using threshold
- ADX Filter: Confirms trend strength
- EMA/SMA Filter: Trend direction confirmation
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█ HOW TO USE
【Recommended Settings】
- Timeframe: 15M, 1H, 4H, Daily
- Neighbors Count: 8 (default)
- Feature Count: 5 for comprehensive analysis
【Signal Interpretation】
- Green BUY label: Long entry signal
- Red SELL label: Short entry signal
- Bar colors: Green (bullish) / Red (bearish) prediction strength
【Trade Statistics Panel】
- Winrate: Historical win percentage
- Trades: Total (Wins|Losses)
- WL Ratio: Win/Loss ratio
- Early Signal Flips: Premature signal changes
【Filter Recommendations】
- Enable Volatility Filter for ranging markets
- Enable Regime Filter for trend confirmation
- Use EMA Filter (200) for higher timeframes
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█ CREDITS
Original Lorentzian Classification concept and MLExtensions library by jdehorty.
Enhanced with visual modifications and alert integration by EduVest.
License: Mozilla Public License 2.0
EDUVEST UTBOT ADJ - Adaptive ATR Trailing StopEDUVEST UTBOT ADJ - Adaptive ATR Trailing Stop with Session-Based Sensitivity
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█ ORIGINALITY
This indicator is an enhanced version of the classic UT Bot concept, featuring automatic session-based ATR sensitivity adjustment. Unlike the original UT Bot which uses a fixed sensitivity value, this version dynamically adapts to different trading sessions (Tokyo, London, New York) and automatically detects asset characteristics to optimize signal generation.
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█ WHAT IT DOES
- Generates BUY and SELL signals based on ATR trailing stop crossovers with a moving average
- Automatically adjusts sensitivity based on current trading session (Tokyo/London/NY)
- Auto-detects asset type and applies optimized parameters for each instrument
- Displays real-time session information and volatility status
- Provides alert functionality with customizable cooldown periods
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█ HOW IT WORKS
【Core Logic: ATR Trailing Stop】
The indicator calculates an ATR-based trailing stop using the formula:
Trailing Stop = Price ± (Sensitivity × ATR)
When price is above the trailing stop and rising, the stop trails below price.
When price is below the trailing stop and falling, the stop trails above price.
【Signal Generation】
- BUY Signal: Price crosses above the trailing stop AND Moving Average crosses above the trailing stop
- SELL Signal: Price crosses below the trailing stop AND Moving Average crosses below the trailing stop
【Session-Based Sensitivity Adjustment】
The indicator adjusts ATR sensitivity based on trading session (JST timezone):
- Tokyo (08:00-15:00): Lower sensitivity (reduced by adjustment value) - typically quieter markets
- London (15:00-23:00): Base sensitivity - moderate volatility
- New York (23:00-08:00): Higher sensitivity (increased by adjustment value) - higher volatility
【Dynamic ATR Adjustment】
When enabled, the indicator compares current ATR to its smoothed average:
- ATR Ratio = Current ATR / SMA(ATR, smoothing period)
- Volatility Multiplier = 1.0 + (Sensitivity × (2.0 - ATR Ratio))
This reduces sensitivity during high volatility (fewer false signals) and increases sensitivity during low volatility (faster response).
【Auto Asset Detection】
The indicator automatically detects the traded instrument and applies optimized parameters:
- Stable pairs (USDJPY, EURUSD, USDCHF): Base sensitivity 1.5-1.8
- Moderate pairs (AUDUSD, USDCAD, EURJPY): Base sensitivity 2.0-2.3
- Volatile pairs (GBPUSD): Base sensitivity 2.8
- Commodities (GOLD/XAUUSD): Base sensitivity 3.5
- Indices (NASDAQ/NAS100): Base sensitivity 4.0
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█ HOW TO USE
【Recommended Settings】
- Timeframe: 15 minutes or higher (15M, 1H, 4H recommended)
- Best performance on: Forex majors, Gold, NASDAQ
- Enable "Auto Asset Detection" for optimized parameters
【Entry Rules】
- BUY: Enter long when green BUY label appears
- SELL: Enter short when pink SELL label appears
【Session Panel】
The top-right panel displays:
- Current trading session (Tokyo/London/NY)
- Volatility status (High Chance/Medium Chance/Caution)
- Mode (AUTO/MANUAL)
【Alert Setup】
1. Enable "Viewer Alert Display" in settings
2. Set cooldown period (default: 15 minutes) to avoid signal spam
3. Create alert with "Any alert() function call" condition
【Important Notes】
- This indicator does not repaint - signals are confirmed at bar close
- Lower timeframes (1M, 5M) may generate excessive signals
- Always use proper risk management and confirm with other analysis
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█ SETTINGS OVERVIEW
🎯 Alert Settings
- Viewer Alert Display: Enable/disable alert labels
- Cooldown Function: Prevent rapid consecutive signals
- Cooldown Time: Minutes between alerts (5-60)
🔧 Dynamic ATR Settings
- Enable Dynamic ATR: Auto-adjust based on volatility
- ATR Period: Calculation period (default: 14)
- ATR Smoothing: Smoothing period for ratio calculation
- Volatility Sensitivity: How much to adjust (0.1-1.0)
🕐 Session ATR Adjustment
- Enable Time Adjustment: Session-based sensitivity
- Show Session Info: Display session panel
📊 Asset Settings
- Auto Asset Detection: Automatically optimize for instrument
- Manual settings available when auto-detection is disabled
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█ CREDITS
Based on the original UT Bot concept by QuantNomad.
Enhanced with session-based adaptation and auto-asset detection by EduVest.
License: Mozilla Public License 2.0
Volatility Trend Score [BackQuant]Volatility Trend Score
Overview
Volatility Trend Score is a trend-strength and regime-evaluation indicator built to measure directional persistence, not just direction. Most trend tools answer “up or down” using slope, crossovers, or a single condition. This indicator answers a more useful question for real trading: “How consistently is trend structure holding up once volatility is accounted for?”
It does this by building a volatility-scaled trailing structure (ATR-based) and then scoring how that structure evolves over a configurable lookback range. The output is a continuous score that rises when trend is persistent and decays when price action becomes noisy, mean-reverting, or unstable.
What it is measuring (the real goal)
This indicator is not trying to predict reversals. It is trying to quantify whether the market is behaving like a trend market or a chop market. It focuses on:
Persistence: does structure keep pushing in one direction bar after bar?
Stability: are pullbacks being absorbed without breaking the trailing structure?
Regime: is the market trending strongly enough to justify directional bias?
If you already have entries from other systems, this becomes a high-quality trend filter and trade management layer.
Core idea
At its foundation, the indicator combines two parts:
A volatility-adjusted trailing level derived from ATR and a user-defined factor.
A rolling persistence score that compares the current trail to prior trail values over a configurable loop window.
The trailing structure adapts to volatility and enforces one-sided movement, while the scoring logic converts that behavior into a numeric measure of trend quality.
Inputs and what they actually control
Average True Range Period (calc_p)
Defines the ATR window used to estimate volatility. A higher value smooths the volatility estimate and makes the trailing structure less reactive.
Factor (atr_factor)
Scales the ATR band size. Higher values widen the trailing band, filtering more noise, reducing flip frequency, and generally producing slower but more stable regimes.
For Loop Start/End (start/end)
Defines the comparison window used to build the score. It effectively sets how many historical trail values the current trail is compared against.
Shorter ranges produce a faster, more responsive score.
Longer ranges produce a slower, more “confidence-based” score that only climbs when trend persistence is sustained.
Long/Short Thresholds (thresL/thresS)
Convert a continuous score into regime thresholds.
Long threshold is a “trend quality requirement” for bullish bias.
Short threshold is used as a deterioration / breakdown trigger via crossunder logic.
Volatility-adjusted trailing structure
The trailing line is built from ATR bands around price:
up = close + ATR * factor
dn = close - ATR * factor
Then a trailing value is maintained with one-sided ratcheting behavior:
If dn rises above the previous trail, the trail steps up (ratchets upward).
If up drops below the previous trail, the trail steps down (ratchets downward).
This “ratchet” behavior is important. It prevents the trail from oscillating with small countertrend moves, forcing the trail to represent meaningful structure rather than micro-noise. On-chart, this trail often behaves like dynamic support/resistance in trends.
Why the trail is a better base than raw price
Price itself is noisy, and volatility changes the meaning of “big move” vs “small move.” By anchoring structure to ATR:
A move is interpreted relative to current volatility, not in absolute points.
High-volatility chop is less likely to be misread as a trend.
Trend structure is normalized across assets and timeframes more reliably.
This is why the score remains usable even when switching from low-vol assets to high-vol crypto pairs.
Trend scoring logic
The score is built by repeatedly comparing the current trailing value to trailing values from prior bars across a loop window:
If current trail > trail , add +1
If current trail < trail , add -1
This is a persistence test, not a momentum calculation. In a strong trend, the trail should generally keep stepping in the trend direction, so current values will be greater than many past values (bullish) or lower than many past values (bearish). In chop, the trail fails to progress meaningfully, so the score compresses, oscillates, or bleeds out.
How to interpret the score
Think of the score as a “trend conviction meter”:
High positive values: bullish persistence, structure is advancing consistently.
Low positive values: bullish bias may exist, but trend quality is weak or unstable.
Near zero: indecision, range behavior, or frequent structure challenges.
Negative values: bearish dominance or sustained deterioration in structure.
The speed of score change matters too:
Fast expansion suggests a fresh regime gaining traction.
Slow grind suggests mature trend continuation.
Rapid compression often signals consolidation, exhaustion, or a transition phase.
Signals and regime transitions
This script uses two different styles of conditions (important detail):
Long condition: score > long threshold (state-based, persistent while true).
Short condition: crossunder(score, short threshold) (event-based trigger).
That means:
Long bias can remain active as long as score stays above the long threshold.
Short regime flips are triggered at the moment the score breaks down through the short threshold.
On the chart, long/short shapes are only plotted when the regime flips (first bar of the change), not on every bar, using:
Long shape when signal becomes 1 and previous signal was -1
Short shape when signal becomes -1 and previous signal was 1
This keeps signals clean and avoids spam, making it usable for alerts and regime tagging.
Visual presentation
The indicator is designed to work both as a panel oscillator and as an on-chart overlay:
Score plot (oscillator): color reflects active regime state.
Optional trail on price: volatility-scaled structure line on chart.
Optional threshold reference lines: clear regime boundaries.
Optional candle coloring: makes regime obvious without reading the panel.
Optional background shading: useful for quick scanning and backtesting visually.
You can use only the score, only the trail, or both together depending on your workflow.
Practical use cases
1) Trend filter for systems
Use the score as a regime gate:
Allow long entries only when score is above the long threshold.
Avoid longs when score compresses toward zero or loses the threshold.
Treat the short threshold break as “trend is no longer healthy.”
This often improves system expectancy by reducing exposure during low-conviction conditions.
2) Trend quality grading
Instead of treating all uptrends as equal:
Higher score = higher persistence, better continuation odds.
Score plateau = trend losing pressure, continuation becomes less reliable.
Score decay while price rises = trend is getting weaker under the hood.
This is useful for position sizing or deciding whether to add to winners.
3) Trade management and exits
Two complementary tools exist here:
Trail line can act as a dynamic stop reference or structure invalidation level.
Score behavior can be used to scale out when persistence fades (before a full flip).
Many traders use the trail for “hard structure” and the score for “soft deterioration.”
4) Breakout confirmation vs fakeouts
A breakout that immediately fails to build score is often low quality.
Healthy breakouts usually come with score expansion as structure advances.
Fakeouts often revert quickly, score fails to climb, and regime stays unstable.
Tuning guidelines
These are general behaviors you can expect when adjusting settings:
Higher ATR period and factor: slower regimes, fewer flips, cleaner structure.
Lower ATR period and factor: faster reaction, more sensitivity, more noise risk.
Longer loop range: score becomes more “confidence-based,” slower to change.
Shorter loop range: score becomes more “tactical,” faster but more jittery.
A good way to tune is to pick the trail behavior first (ATR period and factor), then tune the score window (loop) to match how quickly you want “trend conviction” to build.
Market behavior focus
Volatility Trend Score is most valuable in markets where volatility shifts frequently and fake trends are common, especially crypto. It is designed to:
Stay out of low-quality chop where most indicators whipsaw.
Quantify when volatility is being expressed directionally (constructive trend).
Provide a clean regime framework for filtering, alignment, and management.
Summary
Volatility Trend Score converts volatility-adjusted structure into a quantified measure of trend persistence. By combining an ATR-based trailing mechanism with a rolling comparison score, it provides a more reliable read on trend quality than single-condition indicators. It is best used as a regime filter, a trend strength gauge, and a trade management layer, helping you stay aligned with strong directional phases while avoiding low-conviction envir
HMA Trend Scalper V1[wjdtks255]
Overview
This indicator is a high-performance trend-following system optimized for crypto futures trading. It provides clear entry signals and dynamic, real-time risk management tools to help traders stay on the right side of the market.
Key Features
Dynamic Trend Tracking: Uses a specialized HMA (Hull Moving Average) to filter market noise and identify the core trend.
Real-time TP/SL Extension: Unlike static indicators, the Take Profit (TP) and Stop Loss (SL) lines extend candle-by-candle along with the price action.
Clean Chart UI: Lines only exist from the entry point to the current candle, preventing chart clutter.
Automatic Completion: Once the price hits a target, the line stops extending and marks the result (Target Hit or Stop Out).
Trading Strategy (How to Trade)
1. Long Entry (🚀 LONG)
Condition: The price must be above the trend line, and a breakout of the recent 5-candle high must occur with significant volume.
Action: Enter a Long position when the "🚀 LONG" label appears.
Exit: Hold until the price reaches the Cyan (Aqua) TP line or hits the Yellow SL line.
2. Short Entry (💀 SHORT)
Condition: The price must be below the trend line, and a breakdown of the recent 5-candle low must occur with significant volume.
Action: Enter a Short position when the "💀 SHORT" label appears.
Exit: Hold until the price reaches the Cyan (Aqua) TP line or hits the Yellow SL line.
3. Risk Management
Stop Loss: The indicator automatically calculates the optimal SL based on recent volatility (ATR) and swing points.
Take Profit: The TP is set at a calculated ratio to ensure a positive risk-to-reward setup.
Settings
Trend Sensitivity: Adjust the HMA length to match your preferred timeframe (Scalping vs. Swing).
Volume Multiplier: Filter out weak moves by increasing the volume breakout requirement.
Custom Styles: Fully customize line colors, widths, and styles (Solid, Dashed, Dotted) in the settings menu.
Zero-Lag ATR Trend [BackQuant]Zero-Lag ATR Trend
Overview
Zero-Lag ATR Trend is a volatility-adaptive trend-following overlay designed to identify directional market regimes with minimal delay while preserving structural clarity. The indicator combines a zero-lag moving average framework with a zero-lag volatility model to produce a trailing trend line that reacts quickly to meaningful price changes without becoming unstable or overly sensitive.
Unlike conventional ATR-based trend tools that rely on lagging averages and delayed volatility estimates, this indicator applies zero-lag logic to both the trend centerline and the volatility calculation. The result is a trend structure that aligns more closely with real-time price action while still maintaining the discipline required for trend continuation trading.
Core design philosophy
The core idea behind Zero-Lag ATR Trend is simple:
Reduce signal delay without sacrificing trend integrity.
Adapt dynamically to changing volatility regimes.
Provide a single, clean structure that defines trend direction, continuation, and invalidation.
Instead of stacking multiple indicators, the script builds a complete trend framework from two tightly integrated components: a zero-lag trend spine and a zero-lag ATR trailing mechanism.
Zero-lag trend spine
The trend spine is constructed using a zero-lag moving average (ZLMA). This is achieved by applying a corrective step to a traditional moving average, effectively compensating for smoothing delay.
Conceptually, the process works as follows:
A base moving average is calculated from the selected price source.
That moving average is then passed through a zero-lag correction.
The correction pulls the line closer to current price without introducing noise.
This produces a trend line that reacts faster than standard EMA, SMA, or HMA signals, particularly during early trend acceleration phases. Multiple moving-average types can be used inside the zero-lag framework, allowing traders to fine-tune responsiveness based on asset behavior and timeframe.
Zero-lag volatility model
Volatility is measured using True Range, but instead of applying classic ATR smoothing, the indicator uses a zero-lag smoothing pass on the True Range itself.
This approach offers several advantages:
Volatility expands more quickly during impulse moves.
Volatility contracts faster during consolidations.
Band width adjusts in near real-time to changing conditions.
The smoothed zero-lag ATR is multiplied by a user-defined factor to create adaptive upper and lower boundaries around the trend spine. These boundaries define how much counter-movement price is allowed before the trend structure is invalidated.
Volatility-aware trailing structure
The trailing output is the defining feature of the indicator. It behaves as a one-directional trailing structure:
In bullish conditions, the trailing line can only move upward.
In bearish conditions, the trailing line can only move downward.
Minor pullbacks inside the volatility envelope do not flip the trend.
This logic prevents the indicator from reacting to shallow retracements and focuses instead on structural trend changes. Because the trailing behavior is volatility-scaled, the indicator remains stable during high volatility while still responding promptly during regime shifts.
Trend flips and regime transitions
Trend direction is determined by changes in the trailing structure itself rather than raw price crosses. A trend flip occurs only when price movement is strong enough, relative to current volatility, to force the trailing line to reverse direction.
This means:
Bullish flips represent genuine transitions into upward regimes.
Bearish flips represent genuine transitions into downward regimes.
Sideways noise is largely filtered out.
As a result, the indicator is well suited for identifying medium-to-long trend phases rather than short-term oscillations.
Visual structure and chart clarity
The visual design is intentionally minimal and functional:
The main trailing line is color-coded by trend direction.
An optional ribbon or cloud reinforces directional bias.
Optional candle coloring aligns price bars with the active trend.
These elements allow traders to assess trend state instantly without interpreting multiple signals or overlays.
How to use for trend following
Trend bias
Maintain a bullish bias while price holds above the trailing line.
Maintain a bearish bias while price holds below the trailing line.
Entries
Trend flips can be used as initial directional entries.
Pullbacks toward the trailing line often act as continuation opportunities.
Momentum confirmation can be layered on top for additional confluence.
Trend management
The trailing line naturally functions as a dynamic stop reference.
As long as price respects the trailing structure, the trend remains valid.
A flip in direction signals a full regime transition rather than a minor correction.
Why zero-lag matters for trend trading
Traditional trend indicators often react late, especially during fast expansions, resulting in delayed entries and early exits. By reducing lag in both the trend calculation and the volatility model, Zero-Lag ATR Trend aims to capture a larger portion of directional moves while maintaining consistency and discipline.
This makes it particularly effective for momentum-based trend following, breakout continuation strategies, and traders who prioritize staying aligned with dominant market structure rather than predicting reversals.
Summary
Zero-Lag ATR Trend is a complete trend-following framework built around responsiveness, adaptability, and clarity. Its zero-lag architecture allows it to respond earlier to meaningful price changes, while its volatility-aware trailing logic ensures that trends are only invalidated when structure truly breaks. The result is a clean, intuitive tool that supports disciplined trend participation across assets and timeframes.
VWAP Gravity Oscillator (VGO) (Intraday Only)VWAP Gravity Oscillator (VGO)
The VWAP Gravity Oscillator (VGO) is an intraday analytical indicator designed to quantify price displacement from VWAP and the rate of change of that displacement.
The indicator models VWAP as a statistical equilibrium level and evaluates:
Price deviation from VWAP (Delta)
Momentum and acceleration of that deviation via MACD
This framework enables assessment of trend persistence versus mean-reversion pressure in intraday price action.
Methodology
VWAP Delta
Measures the signed distance between price and VWAP, representing directional bias relative to equilibrium.
MACD on Delta
Captures the first- and second-order dynamics of VWAP deviation, highlighting acceleration, deceleration, and potential inflection points.
Zero Line
Represents price–VWAP equilibrium. Crossings may indicate regime transitions.
Interpretation Guidelines
Positive Delta
Price is trading above VWAP with positive directional bias.
Negative Delta
Price is trading below VWAP with negative directional bias.
Increasing MACD
Expansion of VWAP deviation (trend reinforcement).
Decreasing or reversing MACD
Contraction of VWAP deviation (mean-reversion risk).
Intended Applications
Intraday trend validation
Early detection of trend exhaustion
Mean-reversion risk assessment
Filtering low-conviction or balanced market conditions
Implementation Notes
Designed exclusively for intraday timeframes
Automatically suppressed on higher-timeframe charts
Intended as a contextual analysis tool, not a standalone signal generator
Conceptual Summary
VGO evaluates whether price is diverging from, stabilising around, or reverting toward VWAP by analysing both displacement and its rate of change.
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DivScan Pro - User Guide
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OVERVIEW
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DivScan Pro is a multi-indicator divergence scanner that detects potential
reversal points by analyzing 10+ technical indicators simultaneously.
Optimized for 5m and 15m timeframes.
SIGNAL ICONS
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▲ Green Triangle (Below Bar) = BUY Signal
Strong bullish divergence confirmed by volume + RSI oversold
▼ Red Triangle (Above Bar) = SELL Signal
Strong bearish divergence confirmed by volume + RSI overbought
▲ Faded Green Triangle = Weak BUY
Bullish divergence detected but filters not fully met
▼ Faded Red Triangle = Weak SELL
Bearish divergence detected but filters not fully met
H Red "H" Label = Pivot High Point
L Green "L" Label = Pivot Low Point
DIVERGENCE LABELS
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┌─────────┐
│ MC │ Aqua Box (Bottom) = Bullish Divergence
│ RS │ Shows which indicators detected divergence
│ 3 │ Number = total indicator count
└─────────┘
┌─────────┐
│ MC │ Purple Box (Top) = Bearish Divergence
│ VW │ Shows which indicators detected divergence
│ MF │ Number = total indicator count
│ 3 │
└─────────┘
INDICATOR ABBREVIATIONS
────────────────────────────────────────────────────────────────────────────────
MC = MACD Line
MH = MACD Histogram
RS = RSI (Relative Strength Index)
ST = Stochastic
CC = CCI (Commodity Channel Index)
MO = Momentum
OB = OBV (On Balance Volume)
VW = VWMACD (Volume Weighted MACD)
CF = CMF (Chaikin Money Flow)
MF = MFI (Money Flow Index)
EX = External Indicator
DIVERGENCE LINES
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─────── Solid Aqua Line = Bullish Regular Divergence
Price: Lower Low | Indicator: Higher Low
Suggests: Potential upward reversal
─────── Solid Purple Line = Bearish Regular Divergence
Price: Higher High | Indicator: Lower High
Suggests: Potential downward reversal
- - - - Dashed Lime Line = Bullish Hidden Divergence
Price: Higher Low | Indicator: Lower Low
Suggests: Trend continuation (uptrend)
- - - - Dashed Red Line = Bearish Hidden Divergence
Price: Lower High | Indicator: Higher High
Suggests: Trend continuation (downtrend)
HOW TO USE
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1. WAIT FOR STRONG SIGNALS
Look for solid ▲ or ▼ triangles (not faded)
These have volume + RSI confirmation
2. CHECK CONFLUENCE
More indicators = stronger signal
Label shows "3" or higher = high confidence
3. CONFIRM WITH PRICE ACTION
Wait for candle confirmation after signal
Look for support/resistance levels
4. RECOMMENDED SETTINGS FOR SCALPING (5m/15m)
• Pivot Period: 3
• Min Confirmations: 2
• Max Lookback: 50
• Wait Confirmation: ON
SETTINGS QUICK REFERENCE
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MAIN
Pivot Period How many bars to identify pivot (lower = more signals)
Pivot Source Close or High/Low for pivot detection
Divergence Type Regular, Hidden, or Both
Max Pivots Maximum pivot points to scan
Max Lookback Maximum bars to look back
Min Confirmations Minimum indicators required (higher = fewer but stronger)
Wait Confirmation Wait for bar close before signal
DISPLAY
Labels Full (MC), Abbrev (M), or None
Show Count Display number of confirming indicators
Show Lines Draw divergence lines on chart
Show Pivots Mark H/L pivot points
Last Only Show only most recent divergence
Show MA 50/200 Display moving averages
INDICATORS
Toggle each indicator ON/OFF for divergence scanning
ALERTS
────────────────────────────────────────────────────────────────────────────────
Available alerts in TradingView:
• Bullish Regular Divergence
• Bearish Regular Divergence
• Bullish Hidden Divergence
• Bearish Hidden Divergence
• Any Bullish Divergence
• Any Bearish Divergence
TIPS
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✓ Higher "Min Confirmations" = fewer signals but higher accuracy
✓ Use with support/resistance levels for best entries
✓ Strong signals (solid triangles) have better win rate
✓ Multiple indicator confluence (3+) = highest probability trades
✓ Always use stop loss - divergence can fail
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DivScan Pro v1.0
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