Axis-Pro System | Trend Structure + Fibonacci Pullbacks Axis-Pro System is a comprehensive Trend Following strategy designed to trade high-probability pullbacks. Unlike indicators that merely chase price, this system patiently waits for market structure alignment before seeking an entry.
The system is built on the premise of "Quality over Quantity", utilizing volatility and structure filters to avoid choppy markets (ranges) and false breakouts.
🧠 Strategy Logic
The system makes decisions based on a strict 4-step hierarchy:
Higher Timeframe (HTF) Bias:
Analyzes the trend on a higher timeframe to ensure we are trading in the direction of the dominant flow.
Structure & BOS (Break of Structure):
Identifies clear impulses that break previous highs or lows. Once a BOS is confirmed, the system "arms" the trade and waits.
Fibonacci Zone Pullback:
It does not chase the breakout. Instead, it waits for a pullback into the "Discount Zone" (Golden Zone, configurable between 0.382 and 0.618) to improve the Risk/Reward ratio.
Validation & Trigger:
Uses an ATR expansion check to filter out low-volatility periods.
Requires candle confirmation and alignment with fast EMAs before pulling the trigger.
🛡️ Risk Management
The system incorporates advanced position management using a split execution model (50/50):
Dynamic Stop Loss: Automatically calculated using an ATR multiplier or the recent Swing High/Low (whichever offers better protection).
TP1 (Take Profit 1): Closes 50% of the position at a fixed R-multiple (e.g., 1.5R) to lock in profit and moves the Stop Loss to Break-Even.
TP2 (Runner): The remaining 50% is left to run for higher targets (e.g., 3.0R) or until the trend bends, maximizing gains during strong moves.
Trailing Stop: Optional feature to trail price with a fast EMA once the first target is hit.
⚙️ Settings & Features
The script is highly customizable for different assets (Crypto, Forex, Indices):
Date Range Filter: Includes a date selector to perform precise Backtesting on specific periods (e.g., testing specifically during a Bear Market vs. Bull Market).
Auto Trendlines: Automatically draws relevant trendlines for visual support.
Quality Filters: Options to toggle the EMA 200 filter and breakout buffers.
⚠️ Disclaimer
This strategy is a tool for analysis and backtesting purposes. Past performance does not guarantee future results. It is highly recommended to test the strategy on a Demo account first and adjust parameters according to the volatility of the specific asset being traded. Always use responsible risk management. Strategy
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Adaptive Momentum Contextdaptive Momentum Context (AMC)
Adaptive Momentum Context (AMC) is a single-panel, overlay indicator designed to help traders read market context, momentum behavior, and volatility-driven rhythm in a structured and non-misleading way.
This indicator does not aim to predict future price movements. Instead, it focuses on describing current market conditions using adaptive smoothing and higher-timeframe bias.
Concept Overview
AMC is built around three core ideas:
Higher Timeframe Context (Bias)
Adaptive Market Rhythm
Momentum Behavior within Context
These components are combined to provide a clearer view of when momentum aligns with the broader market structure.
Higher Timeframe Bias
The indicator retrieves price data from a user-selected higher timeframe and compares it to a moving average on that timeframe.
When higher timeframe price is above its average, the background is shaded green.
When it is below, the background is shaded red.
This background does not generate signals.
Its purpose is to define directional context and reduce decision-making against dominant market conditions.
Adaptive Market Rhythm
Instead of using a fixed-length moving average, AMC calculates an adaptive smoothing length based on relative volatility.
When volatility expands, the smoothing period increases.
When volatility contracts, the smoothing period shortens.
Because Pine Script does not allow dynamic lengths in built-in moving averages, the adaptive line is calculated manually using a recursive EMA formula.
This ensures:
No repainting
No future data access
Full Pine Script v6 compliance
The adaptive line represents the current market rhythm, not a trend guarantee.
Momentum Behavior
Momentum is derived from changes in the adaptive rhythm rather than raw price.
Small visual markers appear when:
Momentum accelerates in the direction of the higher timeframe bias
Momentum decelerates against that bias
These markers are contextual cues, not standalone trade signals.
How to Use
AMC is best used as a context and filtering tool, not as a mechanical entry system.
Possible use cases:
Filtering lower-timeframe entries
Avoiding trades against higher-timeframe structure
Visualizing momentum shifts during pullbacks or continuations
Users are encouraged to combine this indicator with their own risk management and execution rules.
Important Notes
This indicator does not provide performance guarantees.
Past behavior does not imply future results.
No lookahead, no repainting, or non-standard chart types are used.
Default settings are intended for general use and may require adjustment depending on market and timeframe. Indicator
ICT Flow Matrix [Ultimate]📊 Overview
ICT Flow Matrix is a comprehensive, all-in-one Smart Money Concepts (SMC) indicator built for traders who follow ICT (Inner Circle Trader) methodology. This indicator consolidates over 15 institutional trading concepts into a single, highly customizable tool—eliminating chart clutter from multiple indicators while providing deep market structure analysis.
Whether you're identifying liquidity pools, tracking order flow, or timing entries during ICT Macro windows, this indicator delivers institutional-grade analysis directly on your chart.
Pro Tip: use with ICT Market Regime Detector for clear language reads on everything.
⚡ Key Features
🎯 Price Delivery Arrays (PDAs)
Fair Value Gaps (FVG) — Automatic detection with customizable mitigation tracking (Wick Touch, 50% CE, Full Close)
Inverse FVGs (iFVG) — Identifies when FVGs fail and flip, creating new tradeable zones
Order Blocks (OB) — Last opposing candle before impulsive moves with adjustable impulse strength
Breaker Blocks (BB) — Automatically generated when Order Blocks fail
Rejection Blocks (RB) — Strong wick rejections indicating institutional defense
Volume Imbalances (VIMB) — Gaps between candle bodies showing aggressive institutional activity
📐 Market Structure & Liquidity
Market Structure Shifts (MSS) — Real-time detection of bullish/bearish structure breaks
Equal Highs/Lows (EQH/EQL) — Liquidity pools where stop losses accumulate
Buy-Side/Sell-Side Liquidity (BSL/SSL) — Swing point liquidity levels with sweep detection
Premium/Discount Zones — Visual shading showing institutional buying/selling areas
OTE Zone (61.8%-79%) — Optimal Trade Entry zone for high-probability entries
⏰ Time-Based Analysis
ICT Macro Times — All nine 30-minute algorithmic windows (02:45, 03:45, 04:45, 09:45, 10:45, 13:45, 14:45, 15:15, 15:45 NY Time)
Killzone Sessions — Asia, London, NY AM, NY PM with customizable times
Session Opens — Weekly, Monthly, Daily opening prices
Previous Period H/L — PDH/PDL, PWH/PWL, PMH/PML levels
📏 Dealing Ranges
Multi-Timeframe Ranges — 21-Day, 3-Day, Daily dealing ranges
Session Ranges — Asia, London, NY dealing ranges with equilibrium
Fibonacci Structure — 0%, 50% (EQ), 100% levels with P/D shading
🕯️ HTF Orderflow
Higher Timeframe Candles — Display up to 6 HTF candles with auto-timeframe selection
Candle Timer — Countdown to next HTF candle close
O/H/L Reference Lines — Current HTF open, high, low levels extended on chart
🎨 Visual Customization
5 Theme Presets — Dark Pro, Light Clean, Neon, Classic, Custom
Full Color Control — Customize every element individually
Zone Styles — Filled or Border Only options
Mitigation Effects — Visual fade when zones are mitigated
📋 Smart Dashboard
Real-Time Status — Structure bias, zone position, active session, OTE status
Confluence Score — Algorithmic scoring when multiple concepts align
Zone Counters — Active FVG, OB, BB, RB, VIMB, liquidity levels
3 Display Modes — Minimal, Compact, Detailed
🔔 Comprehensive Alert System
40+ Alert Conditions including:
FVG/OB/BB/RB/VIMB formation
Liquidity sweeps (EQH, EQL, BSL, SSL)
Market Structure Shifts
OTE zone entry
Macro time windows
Session opens
High confluence zones
Combo alerts (Macro + Confluence)
📖 How To Use
For Swing/Position Traders:
Enable HTF Orderflow to identify dominant trend direction
Use Dealing Ranges (3D, 21D) to find premium/discount zones
Look for OB/FVG confluence in discount (longs) or premium (shorts)
Confirm with MSS for trend alignment
For Day/Intraday Traders:
Mark the Asian Range during pre-market
Wait for London or NY AM Killzone
Enter during ICT Macro windows when price reaches FVG/OB in OTE zone
Target opposite liquidity (BSL for longs, SSL for shorts)
Confluence Trading:
Dashboard shows real-time confluence score
Score ≥ 3 indicates multiple ICT concepts aligned
Higher scores = higher probability setups
⚙️ Recommended Settings
Trading Style FVG Max OB Max History Bars HTF Candles
Scalping 3-5 2-3 100-200 3-4 Day Trading 5-8 3-5 200-400 4-5
Swing Trading 8-12 5-8 400-800 5-6
🎯 Best Practices
✅ Do:
Use HTF bias before taking LTF entries
Wait for Macro time windows for highest probability
Combine MSS + FVG/OB + OTE for A+ setups
Let mitigated zones fade (use Mitigation Fade setting)
❌ Avoid:
Trading against HTF structure
Entries outside Killzones (lower probability)
Ignoring liquidity targets
Over-cluttering chart (disable unused features)
📝 Version History
v6.0 (Current)
Complete rewrite in PineScript v6
Added ICT Macro Times with bracket/background styles
Enhanced confluence detection algorithm
Improved HTF candle rendering with multiple styles
Added Inverse FVG detection
Session-based Dealing Ranges
Performance optimizations
40+ alert conditions
⚠️ Disclaimer
This indicator is a technical analysis tool designed to visualize ICT/SMC concepts. It does not provide financial advice or guarantee profitable trades. Past performance is not indicative of future results. Always use proper risk management and trade responsibly.
💬 Support & Feedback
If you find this indicator valuable, please leave a comment or boost! Your feedback helps improve future updates.
Questions? Drop a comment below—I actively respond to all questions about the indicator's features and usage.
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ICT Market Regime Detector [TradeHook]🔮 Overview
The **ICT Market Regime Detector** is an advanced market condition classifier designed to identify the current market environment and provide context-aware trading guidance. Rather than generating buy/sell signals, this indicator focuses on answering the crucial question: *"What type of market am I trading in right now?"*
Understanding market regime is fundamental to successful trading. The same strategy that works brilliantly in a trending market can fail spectacularly during consolidation. This indicator automatically classifies market conditions into one of eight distinct regimes, each requiring different trading approaches.
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🎯 Regime Classifications
The indicator identifies these market states:
| Regime | Description | Recommended Approach |
|------------------------|--------------------------------------------------|--------------------------------------|
| *STRONG TREND* |Directional momen. w/ healthy struc| Cont.entries with OTE pullbacks |
| **WEAK TREND** | Gradual drift with retracements | Conservative Order Block entries |
| **ACCUMULATION** | Institutional buying within range | Longs near range lows |
| **DISTRIBUTION** | Institutional selling within range | Shorts near range highs |
| **CONSOLIDATION** | Tight range, low volatility squeeze | Wait for breakout |
| **EXPANSION** | Volatile breakout phase | Momentum following |
| **REVERSAL** | Structural transition period | Wait for confirmation |
| **CHOPPY** | No clear edge | **Avoid trading** |
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⚙️ How It Works
**Trend Analysis Engine**
- Calculates ADX (Average Directional Index) using Wilder's smoothing method
- Monitors +DI/-DI for directional bias
- Detects trend health via EMA alignment
- Identifies exhaustion through RSI divergence
**Volatility Analysis Engine**
- Measures current vs historical volatility ratio
- Classifies as LOW, NORMAL, HIGH, or EXTREME
- Tracks volatility expansion/contraction phases
**Range Analysis Engine**
- Calculates dynamic support/resistance boundaries
- Tracks price position within range (0-100%)
- Detects range narrowing (squeeze) and expansion patterns
**Institutional Activity Detection**
- Volume spike identification
- Absorption candle patterns (large wicks, small body)
- Displacement candles (large body, small wicks)
- Accumulation/Distribution pattern recognition
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🛡️ Risk Management Features
**Daily Loss Limit**
- Set maximum daily loss as percentage of account
- Visual warning when approaching limit
- Alert when limit is breached
**Maximum Daily Trades**
- Configurable trade counter per session
- Prevents overtrading
- Session reset options (NY Open, London Open, etc.)
**Trading Readiness Checklist**
- Clear regime ✓/✗
- Kill zone active ✓/✗
- HTF alignment ✓/✗
- Volatility normal ✓/✗
- Loss limit OK ✓/✗
- Trades remaining ✓/✗
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📊 Multi-Timeframe Analysis
The indicator includes 4H timeframe regime alignment to ensure lower timeframe setups align with higher timeframe bias. Trades taken with HTF alignment historically have higher probability.
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⏰ Kill Zone Integration
Built-in ICT Kill Zone detection:
- 🌙 Asian Session (Range Building)
- 🇬🇧 London Open (Prime Execution)
- 🇺🇸 NY AM (Prime Execution)
- 🔫 Silver Bullet (10-11 AM EST)
- 🇺🇸 NY PM (Afternoon Opportunities)
Configurable UTC offset for your timezone.
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🎨 Visual Features
- **Regime-Colored Bars** - Instantly see current market state
- **Comprehensive Dashboard** - All metrics in one panel
- **Adjustable Table Size** - Tiny/Small/Normal/Large
- **Flexible Positioning** - Place dashboard in any corner
- **Optional Regime Labels** - Mark regime changes on chart
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⚠️ Important Notes
1. This indicator is a **decision support tool**, not a signal generator
2. Always combine with proper price action analysis
3. Past regime identification doesn't guarantee future performance
4. Risk management settings are for tracking purposes only - actual position management should be done through your broker
5. The indicator works best on liquid markets with consistent volume data
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📚 Educational Purpose
This indicator is designed for educational purposes to help traders understand market structure and regime classification. It implements concepts from ICT (Inner Circle Trader) methodology including:
- Market structure analysis
- Kill zone timing
- Institutional activity patterns
- Multi-timeframe confluence
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🔧 Inputs Summary
**Master Toggles**
- Enable/Disable indicator, regime detection, recommendations, risk management, alerts
**Core Settings**
- Analysis lookback periods (short/medium/long)
- ADX thresholds for trend classification
- Volatility spike multiplier
**Risk Management**
- Max daily loss percentage
- Max daily trades
- Account size for P&L calculation
- Session reset timing
**Visualization**
- Dashboard on/off and position
- Regime zones and labels
- Bar coloring
- Table text size
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💡 Tips for Use
1. **Don't trade CHOPPY regimes** - The indicator explicitly warns when no edge exists
2. **Respect the checklist** - Trade only when multiple conditions align
3. **Adjust ADX thresholds** - Different instruments may require fine-tuning
4. **Monitor regime duration** - Fresh regime changes often present the best opportunities
5. **Use with other TradeHook indicators** - Designed to complement the MTMGBS system
⚖️ DISCLAIMER
This indicator is for **educational and informational purposes only**. It does not constitute financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always conduct your own analysis and consult with a qualified financial advisor before making trading decisions.
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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.
Indicator
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.
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Strategy
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. Indicator
Indicator
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.
Indicator
Indicator
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!
Indicator
Indicator
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.
Indicator
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.
Indicator
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 PulseWire.
🟢 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.
Indicator
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 Indicator
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 Indicator
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
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