Impulse Trend Levels [BOSWaves]Impulse Trend Levels - Momentum-Adaptive Trend Detection with Impulse-Driven Confidence Bands
Overview
Impulse Trend Levels is a momentum-aware trend identification system that tracks directional price movement through adaptive confidence bands, where band width dynamically adjusts based on impulse strength and freshness to reflect real-time conviction in the current trend direction.
Instead of relying on fixed moving average crossovers or static band multipliers, trend state, band positioning, and zone thickness are determined through impulse detection patterns, exponential decay modeling, and volatility-normalized momentum measurement.
This creates dynamic trend boundaries that reflect actual momentum intensity rather than arbitrary technical levels - contracting during fresh impulse conditions when trend conviction is high, expanding during impulse decay periods when directional confidence weakens, and incorporating momentum freshness calculations to reveal whether trends are accelerating or deteriorating.
Price is therefore evaluated relative to bands that adapt to momentum state rather than conventional static thresholds.
Conceptual Framework
Impulse Trend Levels is founded on the principle that meaningful trend signals emerge when price momentum intensity reaches significant thresholds relative to recent volatility rather than when price simply crosses moving averages.
Traditional trend-following methods identify directional changes through price-indicator crossovers, which often ignore the underlying momentum dynamics and conviction levels that sustain those moves. This framework replaces static-threshold logic with impulse-driven band construction informed by actual momentum strength and decay characteristics.
Three core principles guide the design:
Trend direction should be determined by volatility-normalized momentum breaches, not simple price crossovers alone.
Band width must adapt to impulse freshness, reflecting real-time confidence in the current trend.
Momentum decay modeling reveals whether trends are maintaining strength or losing conviction.
This shifts trend analysis from static indicator levels into adaptive, momentum-anchored confidence boundaries.
Theoretical Foundation
The indicator combines exponential moving average smoothing, mean absolute deviation measurement, impulse detection methodology, and exponential decay tracking.
An EMA-based trend baseline provides directional reference, while Mean Absolute Deviation (MAD) offers volatility-normalized scaling for momentum measurement. Impulse detection identifies significant price movements relative to recent volatility, triggering fresh momentum readings that decay exponentially over time. Band multipliers interpolate between tight and wide settings based on calculated impulse freshness.
Four internal systems operate in tandem:
Trend Baseline Engine : Computes EMA-smoothed price levels for directional reference and band anchoring.
Volatility Measurement System : Calculates MAD to provide adaptive scaling that normalizes momentum across varying market conditions.
Impulse Detection Logic : Identifies volatility-normalized price movements exceeding threshold levels, capturing momentum intensity and direction.
Decay-Based Confidence Modeling : Applies exponential decay to impulse readings, converting raw momentum into time-weighted freshness metrics that drive band adaptation.
This design allows trend confidence to reflect actual momentum behavior rather than reacting mechanically to price formations.
How It Works
Impulse Trend Levels evaluates price through a sequence of momentum-aware processes:
Baseline Calculation : EMA smoothing of open and close creates a directional trend reference that filters short-term noise.
Volatility Normalization : MAD calculation over a specified lookback provides dynamic scaling for momentum measurement.
Raw Impulse Detection : Price change over impulse lookback divided by MAD creates volatility-normalized momentum readings.
Threshold-Based Activation : When normalized momentum exceeds threshold (1.0), impulse registers with absolute magnitude and directional sign.
Exponential Decay Application : Between impulse events, stored impulse value decays exponentially via configurable decay rate.
Freshness Conversion : Decaying impulse transforms into freshness metric (0-100%) representing current momentum conviction.
Adaptive Band Construction : Band multiplier interpolates between minimum (fresh) and maximum (stale) settings based on freshness, then scales MAD to determine band width.
Trend State Logic : Price crossing above upper band triggers bullish state; crossing below lower band triggers bearish state; state persists until opposite breach.
Signal Generation : Trend state switches from bearish to bullish produce buy signals; bullish to bearish switches produce sell signals.
Retest Identification : Price touching inner band edge after signal buffer period marks retests, with cooldown periods preventing excessive plotting.
Together, these elements form a continuously updating trend framework anchored in momentum reality.
Interpretation
Impulse Trend Levels should be interpreted as momentum-anchored trend confidence boundaries:
Bullish Trend State (Cyan) : Established when price closes above adaptive upper band, indicating upward momentum breach with associated confidence level.
Bearish Trend State (Magenta) : Established when price closes below adaptive lower band, signaling downward momentum breach with directional conviction.
Trend Cloud : Visual gradient zone displays between outer and inner band edges, with opacity reflecting current trend state and confidence.
Band Width Dynamics : Tighter bands indicate fresh impulse (high confidence), wider bands indicate impulse decay (reduced confidence).
▲ Buy Signals : Green upward triangles mark bullish trend state initiations at crossovers above upper band.
▼ Sell Signals : Red downward triangles mark bearish trend state initiations at crossovers below lower band.
✦ Retest Markers : Small diamonds identify price retouching inner band edge after sufficient buffer period from initial signal.
Retest Extension Lines : Horizontal projections from retest points extend forward, marking potential support/resistance levels.
Colored Candles : Optional bar coloring reflects current trend state for immediate visual reference. Note: The original chart candles must be disabled in chart settings for the trend-colored candles to display properly.
Impulse freshness, band width dynamics, and momentum normalization outweigh isolated price movements.
Signal Logic & Visual Cues
Impulse Trend Levels presents two primary interaction signals:
Buy Signal (▲) : Green label appears when trend state switches from bearish to bullish via upper band crossover, suggesting momentum shift to upside.
Sell Signal (▼) : Red label displays when trend state switches from bullish to bearish via lower band crossunder, indicating momentum shift to downside.
Retest detection provides secondary confirmation when price revisits inner band boundaries after signal buffer cooldown expires.
Alert generation covers trend state switches (long/short), retest occurrences, and impulse freshness decay below 50% threshold for systematic monitoring.
Strategy Integration
Impulse Trend Levels fits within momentum-informed and adaptive trend-following approaches:
Momentum-Confirmed Entries : Use band crossovers as high-probability trend initiation points where volatility-normalized momentum exceeded threshold.
Freshness-Based Position Sizing : Scale exposure based on impulse freshness - larger positions during fresh impulse periods, reduced sizing as impulse decays.
Band-Width Risk Management : Expect wider price ranges when bands expand during decay, tighter ranges when bands contract during fresh impulse.
Retest-Based Re-entry : Use inner band retests as lower-risk entry opportunities within established trends after initial signal cooldown.
Cloud-Aligned Directional Bias : Favor trades aligning with current trend state rather than counter-trend positions.
Multi-Timeframe Momentum Confirmation : Apply higher-timeframe impulse trend state to filter lower-timeframe entry precision.
Technical Implementation Details
Core Engine : EMA-based baseline with MAD volatility measurement
Impulse Model : Volatility-normalized momentum detection with directional sign capture
Decay System : Exponential decay application (0.8-0.99 range) with freshness conversion
Band Construction : Linear interpolation between min/max multipliers scaled by MAD
Visualization : Gradient-filled cloud zones with bar coloring and signal labels
Signal Logic : State-switch detection with retest buffer and cooldown mechanisms
Performance Profile : Optimized for real-time execution across all timeframes
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-trend detection for scalping with responsive impulse settings
15 - 60 min : Intraday momentum tracking with balanced decay characteristics
4H - Daily : Swing-level trend identification with sustained impulse persistence
Suggested Baseline Configuration:
Trend Length : 19
Impulse Lookback : 5
Decay Rate : 0.99
MAD Length : 20
Band Min (Fresh) : 1.5
Band Max (Stale) : 1.9
Signal Buffer Period : 10
Show Trend Cloud : Enabled
Color Bars : Enabled (requires disabling original chart candles in chart settings)
Show Buy/Sell Signals : Enabled
These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volatility profile, momentum 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:
Excessive signal noise : Increase Trend Length to demand smoother baseline crossovers or increase Impulse Lookback for less reactive momentum detection.
Missed momentum shifts : Decrease Impulse Lookback to capture shorter-term momentum changes or reduce Decay Rate to allow faster impulse fade.
Bands too tight/wide : Adjust Band Min and Band Max multipliers to modify confidence zone thickness across freshness spectrum.
Impulse decays too quickly : Increase Decay Rate toward 0.99 to sustain impulse readings longer between fresh events.
Impulse decays too slowly : Decrease Decay Rate toward 0.8 for faster momentum fade and more frequent band expansion.
Unstable volatility scaling : Increase MAD Length to smooth volatility measurement and reduce sensitivity to short-term spikes.
Too many retest markers : Increase retest cooldown period (55 bars hardcoded) or increase Signal Buffer Period to space out signals.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Trending markets with clear momentum phases and directional persistence
Instruments with consistent volatility characteristics where MAD scaling normalizes effectively
Momentum continuation strategies entering on fresh impulse signals
Trend-following approaches benefiting from adaptive confidence measurement
Reduced Effectiveness:
Choppy, range-bound markets with frequent whipsaw crossovers
Extremely low volatility environments where impulse threshold becomes difficult to breach
News-driven or gapped markets with discontinuous momentum patterns
Mean-reversion dominant conditions where momentum breaches quickly reverse
Consolidation and sideways price action where trend-following methodologies inherently struggle due to lack of sustained directional movement
Integration Guidelines
Confluence : Combine with BOSWaves structure, volume analysis, or traditional trend indicators
Freshness Respect : Trust signals occurring during high impulse freshness periods with contracted bands
Decay Awareness : Reduce position sizing or tighten stops as impulse decays and bands widen
Retest Utilization : Treat inner band retests as continuation confirmation rather than reversal signals
State Discipline : Maintain directional bias aligned with current trend state until opposite band breach occurs
Disclaimer
Impulse Trend Levels is a professional-grade momentum and trend analysis tool. It uses volatility-normalized impulse detection with exponential decay modeling but does not predict future price movements. Results depend on market conditions, volatility characteristics, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management.
Boswaves
Adaptive RSI [BOSWaves]Adaptive RSI - Percentile-Based Momentum Detection with Dynamic Regime Thresholds
Overview
Adaptive RSI is a self-calibrating momentum oscillator that identifies overbought and oversold conditions through historical percentile analysis, constructing dynamic threshold boundaries that adjust to evolving market volatility and momentum characteristics.
Instead of relying on traditional fixed RSI levels (30/70 or 20/80) or static overbought/oversold zones, regime detection, threshold placement, and signal generation are determined through rolling percentile calculation, smoothed momentum measurement, and divergence pattern recognition.
This creates adaptive boundaries that reflect actual momentum distribution rather than arbitrary fixed levels - tightening during low-volatility consolidation periods, widening during trending environments, and incorporating divergence analysis to reveal momentum exhaustion or continuation patterns.
Momentum is therefore evaluated relative to its own historical context rather than universal fixed thresholds.
Conceptual Framework
Adaptive RSI is founded on the principle that meaningful momentum extremes emerge relative to recent price behavior rather than at predetermined numerical levels.
Traditional RSI implementations identify overbought and oversold conditions using fixed thresholds that remain constant regardless of market regime, often generating premature signals in strong trends or missing reversals in range-bound markets. This framework replaces static threshold logic with percentile-driven adaptive boundaries informed by actual momentum distribution.
Three core principles guide the design:
Threshold placement should correspond to historical momentum percentiles, not fixed numerical levels.
Regime detection must adapt to current market volatility and momentum characteristics.
Divergence patterns reveal momentum exhaustion before price reversal becomes visible.
This shifts oscillator analysis from universal fixed levels into adaptive, context-aware regime boundaries.
Theoretical Foundation
The indicator combines smoothed RSI calculation, rolling percentile tracking, adaptive threshold construction, and multi-pattern divergence detection.
A Hull Moving Average (HMA) pre-smooths the price source to reduce noise before RSI computation, which then undergoes optional post-smoothing using configurable moving average types. Confirmed oscillator values populate a rolling historical buffer used for percentile calculation, establishing upper and lower thresholds that adapt to recent momentum distribution. Regime state persists until the oscillator crosses the opposing threshold, preventing whipsaw during consolidation. Pivot detection identifies swing highs and lows in both price and oscillator values, enabling regular divergence pattern recognition through comparative analysis.
Five internal systems operate in tandem:
Smoothed Momentum Engine : Computes HMA-preprocessed RSI with optional post-smoothing using multiple MA methodologies (SMA, EMA, HMA, WMA, DEMA, RMA, LINREG, TEMA).
Historical Buffer Management : Maintains a rolling array of confirmed oscillator values for percentile calculation with configurable lookback depth.
Percentile Threshold Calculation : Determines upper and lower boundaries by extracting specified percentile values from sorted historical distribution.
Persistent Regime Detection : Establishes bullish/bearish/neutral states based on threshold crossings with state persistence between signals.
Divergence Pattern Recognition : Identifies regular bullish and bearish divergences through synchronized pivot analysis of price and oscillator values with configurable range filtering.
This design allows momentum interpretation to adapt to market conditions rather than reacting mechanically to universal thresholds.
How It Works
Adaptive RSI evaluates momentum through a sequence of self-calibrating processes:
Source Pre-Smoothing: Input price undergoes 4-period HMA smoothing to reduce bar-to-bar noise before oscillator calculation.
RSI Calculation: Standard RSI computation applied to smoothed source over configurable length period.
Optional Post-Smoothing: Raw RSI value undergoes additional smoothing using selected MA type and length for cleaner regime detection.
Historical Buffer Population: Confirmed oscillator values accumulate in a rolling array with size limit determined by adaptive lookback parameter.
Percentile Threshold Extraction: Array sorts on each bar to calculate upper percentile (bullish threshold) and lower percentile (bearish threshold) values.
Regime State Persistence: Bullish regime activates when oscillator crosses above upper threshold, bearish regime activates when crossing below lower threshold, neutral regime persists until directional threshold breach.
Pivot Identification: Swing highs and lows detected in both oscillator and price using configurable left/right parameters.
Divergence Pattern Matching: Compares pivot relationships between price and oscillator within min/max bar distance constraints to identify regular bullish (price LL, oscillator HL) and bearish (price HH, oscillator LH) divergences.
Together, these elements form a continuously updating momentum framework anchored in statistical context.
Interpretation
Adaptive RSI should be interpreted as context-aware momentum boundaries:
Bullish Regime (Blue): Activated when oscillator crosses above upper percentile threshold, indicating momentum strength relative to recent distribution favors upside continuation.
Bearish Regime (Red): Established when oscillator crosses below lower percentile threshold, identifying momentum weakness relative to recent distribution favors downside continuation.
Upper Threshold Line (Blue)**: Dynamic resistance level calculated from upper percentile of historical oscillator distribution - adapts higher during trending markets, lower during ranging conditions.
Lower Threshold Line (Red): Dynamic support level calculated from lower percentile of historical oscillator distribution - adapts lower during downtrends, higher during consolidation.
Regime Fill: Gradient coloring between oscillator and baseline (50) visualizes current momentum intensity - stronger color indicates greater distance from neutral.
Extreme Bands (15/85): Upper and lower extreme zones with strength-modulated transparency reveal momentum extremity - darker shading during powerful moves, lighter during moderate momentum.
Divergence Lines: Connect price and oscillator pivots when divergence pattern detected, appearing on both price chart and oscillator pane for confluence identification.
Reversal Markers (✦): Diamond signals appear at 80+ (bearish extreme) and sub-15 (bullish extreme) levels, marking potential exhaustion zones independent of regime state.
Percentile context, divergence confirmation, and regime persistence outweigh isolated oscillator readings.
Signal Logic & Visual Cues
Adaptive RSI presents four primary interaction signals:
Regime Switch - Long : Oscillator crosses above upper percentile threshold after previously being in bearish or neutral regime, suggesting momentum strength shift favoring bullish continuation.
Regime Switch - Short : Oscillator crosses below lower percentile threshold after previously being in bullish or neutral regime, indicating momentum weakness shift favoring bearish continuation.
Regular Bullish Divergence (𝐁𝐮𝐥𝐥) : Price forms lower low while oscillator forms higher low, revealing positive momentum divergence during downtrends - often precedes reversal or consolidation.
Regular Bearish Divergence (𝐁𝐞𝐚𝐫) : Price forms higher high while oscillator forms lower high, revealing negative momentum divergence during uptrends - often precedes reversal or correction.
Alert generation covers regime switches, threshold crossings, and divergence detection for systematic monitoring.
Strategy Integration
Adaptive RSI fits within momentum-informed and mean-reversion trading approaches:
Adaptive Regime Following : Use threshold crossings as primary trend inception signals where momentum confirms directional breakouts within statistical context.
Divergence-Based Reversals : Enter counter-trend positions when divergence patterns appear at extreme oscillator levels (above 80 or below 20) for high-probability mean-reversion setups.
Threshold-Aware Scaling : Recognize that tighter percentile spreads (e.g., 45/50) generate more signals suitable for ranging markets, while wider spreads (e.g., 30/70) filter for stronger trend confirmation.
Extreme Zone Confluence : Combine reversal markers (✦) with divergence signals for maximum-conviction exhaustion entries.
Multi-Timeframe Regime Alignment : Apply higher-timeframe regime context to filter lower-timeframe entries, taking only setups aligned with dominant momentum direction.
Smoothing Optimization : Increase smoothing length in choppy markets to reduce false signals, decrease in trending markets for faster response.
Technical Implementation Details
Core Engine : HMA-preprocessed RSI with configurable smoothing (SMA, HMA, EMA, WMA, DEMA, RMA, LINREG, TEMA)
Adaptive Model : Rolling percentile calculation over confirmed oscillator values with size-limited historical buffer
Threshold Construction : Linear interpolation percentile extraction from sorted distribution array
Regime Detection : State-persistent threshold crossing logic with confirmed bar validation
Divergence Engine : Pivot-based pattern matching with range filtering and duplicate prevention
Visualization : Gradient-filled regime zones, adaptive threshold lines, strength-modulated extreme bands, dual-pane divergence lines
Performance Profile : Optimized for real-time execution with efficient array management and minimal computational overhead
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-structure momentum detection for scalping and intraday reversals
15 - 60 min : Intraday regime identification with divergence-validated turning points
4H - Daily : Swing and position-level momentum analysis with macro divergence context
Suggested Baseline Configuration:
RSI Length : 18
Source : Close
Smooth Oscillator : Enabled
Smoothing Length : 20
Smoothing Type : SMA
Adaptive Lookback : 1000
Upper Percentile : 50
Lower Percentile : 45
Divergence Pivot Left : 15
Divergence Pivot Right : 15
Min Pivot Distance : 5
Max Pivot Distance : 60
These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volatility profile, momentum 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:
Too many whipsaw signals : Widen percentile spread (e.g., 40/60 instead of 45/50) to demand stronger momentum confirmation, or increase "Smoothing Length" to filter noise.
Missing legitimate regime changes : Tighten percentile spread (e.g., 48/52 instead of 45/50) for earlier detection, or decrease "Smoothing Length" for faster response.
Oscillator too choppy : Increase "Smoothing Length" for cleaner readings, or switch "Smoothing Type" to RMA/TEMA for heavier smoothing.
Thresholds not adapting properly : Reduce "Adaptive Lookback" to emphasize recent behavior (500-800 bars), or increase it for more stable thresholds (1500-2000 bars).
Too many divergence signals : Increase "Pivot Left/Right" values to demand stronger swing confirmation, or widen "Min Pivot Distance" to space out detections.
Missing significant divergences : Decrease "Pivot Left/Right" for faster pivot detection, or increase "Max Pivot Distance" to compare more distant swings.
Prefer different momentum sensitivity : Adjust "RSI Length" - lower values (10-14) for aggressive response, higher values (21-28) for smoother trend confirmation.
Divergences appearing too late : Reduce "Pivot Right" parameter to detect divergences closer to current price action.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Markets with mean-reverting characteristics and consistent momentum cycles
Instruments where momentum extremes reliably precede reversals or consolidations
Ranging environments where percentile-based thresholds adapt to volatility contraction
Divergence-driven strategies targeting momentum exhaustion before price confirmation
Reduced Effectiveness:
Extremely strong trending markets where oscillator remains persistently extreme
Low-liquidity environments with erratic momentum readings
News-driven or gapped markets where momentum disconnects from price temporarily
Markets with regime shifts faster than adaptive lookback can recalibrate
Integration Guidelines
Confluence : Combine with BOSWaves structure, volume analysis, or traditional support/resistance
Threshold Respect : Trust signals that occur after clean threshold crossings with sustained momentum
Divergence Context : Prioritize divergences appearing at extreme oscillator levels (80+/15-) over those in neutral zones
Regime Awareness : Consider whether current market regime matches historical momentum patterns used for calibration
Multi-Pattern Confirmation : Seek divergence patterns coinciding with reversal markers or threshold rejections for maximum conviction
Disclaimer
Adaptive RSI is a professional-grade momentum and divergence analysis tool. It uses percentile-based threshold calculation that adapts to recent market behavior but cannot predict future regime shifts or guarantee reversal timing. Results depend on market conditions, parameter selection, lookback period appropriateness, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management.
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.
Delta Reaction Zones [BOSWaves]Delta Reaction Zones - Cumulative Delta-Based Supply and Demand Identification with Flow-Weighted Zone Construction
Overview
Delta Reaction Zones is a volume flow-aware supply and demand detection system that identifies price levels where significant buying or selling pressure accumulated, constructing adaptive zones around cumulative delta extremes with intelligent flow composition analysis.
Instead of relying on traditional price-based support and resistance or fixed pivot structures, zone placement, thickness, and directional characterization are determined through delta accumulation patterns, volatility-adaptive sizing, and the proportional composition of positive versus negative volume flow.
This creates dynamic reaction boundaries that reflect actual order flow imbalances rather than arbitrary price levels - contracting during low volatility environments, expanding during elevated volatility periods, and incorporating flow composition statistics to reveal whether zones formed under buying or selling dominance.
Price is therefore evaluated relative to zones anchored at delta extremes rather than conventional technical levels.
Conceptual Framework
Delta Reaction Zones is founded on the principle that meaningful support and resistance emerge where cumulative volume flow reaches local extremes rather than where price alone forms patterns.
Traditional support and resistance methods identify turning points through price structure, which often ignores the underlying order flow dynamics that drive those reversals. This framework replaces price-centric logic with delta-driven zone construction informed by actual buying and selling pressure.
Three core principles guide the design:
Zone placement should correspond to cumulative delta extremes, not price pivots alone.
Zone thickness must adapt to current market volatility conditions.
Flow composition context reveals whether zones formed under accumulation or distribution.
This shifts supply and demand analysis from static price levels into adaptive, flow-anchored reaction boundaries.
Theoretical Foundation
The indicator combines delta proxy methodology, cumulative volume tracking, adaptive volatility measurement, and flow decomposition analysis.
A signed volume delta proxy estimates directional order flow on each bar, which accumulates into a running cumulative delta series. Pivot detection identifies local extremes in either cumulative delta or its rate of change, marking levels where flow momentum reached inflection points. Average True Range (ATR) provides volatility-responsive zone sizing, while impulse window analysis decomposes recent flow into positive and negative components with percentage weighting.
Four internal systems operate in tandem:
Delta Accumulation Engine : Computes smoothed signed volume and maintains cumulative delta tracking for directional flow measurement.
Pivot Detection System : Identifies significant turning points in cumulative delta or delta rate of change to anchor zone placement.
Adaptive Zone Construction : Scales zone thickness dynamically using ATR-based volatility measurement around pivot anchors.
Flow Composition Analysis : Calculates positive and negative flow percentages over a configurable impulse window to characterize zone formation context.
This design allows zones to reflect actual order flow behavior rather than reacting mechanically to price formations.
How It Works
Delta Reaction Zones evaluates price through a sequence of flow-aware processes:
Signed Volume Delta Calculation : Each bar's volume is directionally signed based on close-open relationship, creating a proxy for buying versus selling pressure.
Cumulative Delta Tracking : Signed volume accumulates into a running total, revealing sustained directional flow over time.
Pivot Identification : Local highs and lows in cumulative delta (or its rate of change) mark significant flow inflection points where zones anchor.
Volatility-Adaptive Sizing : ATR multiplier determines zone half-width, automatically adjusting thickness to current market conditions.
Flow Decomposition : Positive and negative volume components are separated and percentage-weighted over the impulse window to reveal dominant flow direction.
Intelligent Zone Merging : Overlapping zones of the same type automatically merge into broader reaction areas, with flow statistics blended proportionally.
Dynamic Extension and Visualization : Zones extend forward with gradient-filled composition segments showing buy versus sell flow proportions.
Breach Detection and Cleanup : Zones invalidate automatically when price closes beyond their boundaries, maintaining chart clarity.
Together, these elements form a continuously updating supply and demand framework anchored in order flow reality.
Interpretation
Delta Reaction Zones should be interpreted as flow-anchored supply and demand boundaries:
Support Zones (Green) : Form at cumulative delta lows, marking levels where selling exhaustion or buying accumulation occurred.
Resistance Zones (Red) : Establish at cumulative delta highs, identifying areas where buying exhaustion or selling distribution dominated.
Flow Composition Segments : Visual gradient within each zone reveals the buy/sell flow proportion during zone formation. The upper segment (red tint) represents negative (selling) flow percentage while the lower segment (green tint) represents positive (buying) flow percentage.
BUY FLOW / SELL FLOW / MIXED Labels : Indicate dominant flow character when one direction exceeds 60% of total impulse window activity.
Net Delta Statistics : Display cumulative flow totals (Δ) alongside percentage breakdowns for immediate context.
Zone Thickness : Reflects current volatility environment - wider zones in volatile conditions, tighter zones in calm markets.
Zone Merging : Multiple nearby pivots consolidate into broader reaction areas, weighted by their respective flow magnitudes.
Flow composition, volatility context, and delta magnitude outweigh isolated price reactions.
Signal Logic & Visual Cues
Delta Reaction Zones presents two primary interaction signals:
Support Reclaim (RC) : Green label appears when price crosses back above a support zone's midline after trading below it, suggesting renewed buying interest.
Resistance Re-enter (RE) : Red label displays when price crosses back below a resistance zone's midline after trading above it, indicating resumed selling pressure.
Alert generation covers zone creation and midline reclaim/re-entry events for systematic monitoring.
Strategy Integration
Delta Reaction Zones fits within order flow-informed and supply/demand trading approaches:
Flow-Anchored Entry Zones : Use zones as high-probability reaction areas where historical order flow imbalances occurred.
Composition-Based Bias : Favor trades aligning with dominant flow character - long setups near zones formed under buying dominance, short setups near selling-dominated zones.
Volatility-Aware Targeting : Expect wider reaction ranges when ATR expands zones, tighter ranges when ATR contracts them.
Merge-Informed Conviction : Broader merged zones represent multiple flow inflection points, potentially offering stronger support/resistance.
Midline Reclaim Validation : Use RC/RE signals as confirmation of zone respect rather than standalone entry triggers.
Multi-Timeframe Flow Context : Apply higher-timeframe delta zones to inform lower-timeframe entry precision.
Technical Implementation Details
Core Engine : Signed volume delta proxy with EMA smoothing
Accumulation Model : Persistent cumulative delta tracking with optional rate-of-change pivot detection
Zone Construction : ATR-scaled thickness around pivot anchors
Flow Analysis : Positive/negative decomposition over configurable impulse window
Visualization : Gradient-filled zones with embedded flow statistics and percentage segments
Signal Logic : Midline crossover detection with breach-based invalidation
Merge System : Proximity-based consolidation with weighted flow blending
Performance Profile : Optimized for real-time execution with configurable zone limits
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Micro-structure flow zones for scalping and short-term reversals
15 - 60 min : Intraday supply/demand identification with flow context
4H - Daily : Swing-level reaction zones with macro flow characterization
Suggested Baseline Configuration:
Delta Smoothing Length : 3
Pivot Length : 12
Pivot Source : Cumulative Delta
Impulse Window : 100
ATR Length : 14
ATR Multiplier : 0.35 (reduce for lower timeframes)
Maximum Zones : 8
Merge Overlapping Zones : Enabled
Merge Gap : 20 ticks
These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volume profile, tick structure, and preferred zone density, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Zones appearing oversized : Reduce ATR Multiplier to tighten zone thickness, especially on lower timeframes.
Excessive zone clutter : Increase Pivot Length to demand stronger delta extremes before zone creation.
Unstable delta readings : Increase Delta Smoothing Length to reduce bar-to-bar noise in flow calculation.
Missing significant levels : Decrease Pivot Length or switch Pivot Source to "Cumulative Delta RoC" for flow acceleration sensitivity.
Flow percentages feel stale : Reduce Impulse Window Length to emphasize more recent buying/selling composition.
Too many merged zones : Decrease Merge Gap (ticks) or disable merging to preserve individual pivot zones.
Adjustments should be incremental and evaluated across multiple session types rather than isolated market conditions.
Performance Characteristics
High Effectiveness:
Markets with consistent volume and order flow characteristics
Instruments where delta proxy correlates well with actual tape reading
Mean-reversion strategies targeting flow exhaustion zones
Trend continuation entries at zones aligned with dominant flow direction
Reduced Effectiveness:
Extremely low volume environments where delta proxy becomes unreliable
News-driven or gapped markets with discontinuous flow
Highly manipulated or illiquid instruments with erratic volume patterns
Integration Guidelines
Confluence : Combine with BOSWaves structure, market profile, or traditional supply/demand analysis
Flow Respect : Trust zones formed with strong net delta magnitude and clear flow dominance
Context Awareness : Consider whether current market regime matches zone formation conditions
Merge Recognition : Treat merged zones as higher-conviction areas due to multiple flow inflections
Breach Discipline : Exit zone-based setups cleanly when price invalidates boundaries
Disclaimer
Delta Reaction Zones is a professional-grade order flow and supply/demand analysis tool. It uses a volume-based delta proxy that estimates directional pressure but does not access true order book data. 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, volatility context, and comprehensive risk management.
Adaptive ML Trailing Stop [BOSWaves]Adaptive ML Trailing Stop – Regime-Aware Risk Control with KAMA Adaptation and Pattern-Based Intelligence
Overview
Adaptive ML Trailing Stop is a regime-sensitive trailing stop and risk control system that adjusts stop placement dynamically as market behavior shifts, using efficiency-based smoothing and pattern-informed biasing.
Instead of operating with fixed ATR offsets or rigid trailing rules, stop distance, responsiveness, and directional treatment are continuously recalculated using market efficiency, volatility conditions, and historical pattern resemblance.
This creates a live trailing structure that responds immediately to regime change - contracting during orderly directional movement, relaxing during rotational conditions, and applying probabilistic refinement when pattern confidence is present.
Price is therefore assessed relative to adaptive, condition-aware trailing boundaries rather than static stop levels.
Conceptual Framework
Adaptive ML Trailing Stop is founded on the idea that effective risk control depends on regime context rather than price location alone.
Conventional trailing mechanisms apply constant volatility multipliers, which often results in trend suppression or delayed exits. This framework replaces static logic with adaptive behavior shaped by efficiency state and observed historical outcomes.
Three core principles guide the design:
Stop distance should adjust in proportion to market efficiency.
Smoothing behavior must respond to regime changes.
Trailing logic benefits from probabilistic context instead of fixed rules.
This shifts trailing stops from rigid exit tools into adaptive, regime-responsive risk boundaries.
Theoretical Foundation
The indicator combines adaptive averaging techniques, volatility-based distance modeling, and similarity-weighted pattern analysis.
Kaufman’s Adaptive Moving Average (KAMA) is used to quantify directional efficiency, allowing smoothing intensity and stop behavior to scale with trend quality. Average True Range (ATR) defines the volatility reference, while a K-Nearest Neighbors (KNN) process evaluates historical price patterns to introduce directional weighting when appropriate.
Three internal systems operate in tandem:
KAMA Efficiency Engine : Evaluates directional efficiency to distinguish structured trends from range conditions and modulate smoothing and stop behavior.
Adaptive ATR Stop Engine : Expands or contracts ATR-derived stop distance based on efficiency, tightening during strong trends and widening in low-efficiency environments.
KNN Pattern Influence Layer : Applies distance-weighted historical pattern outcomes to subtly influence stop placement on both sides.
This design allows stop behavior to evolve with market context rather than reacting mechanically to price changes.
How It Works
Adaptive ML Trailing Stop evaluates price through a sequence of adaptive processes:
Efficiency-Based Regime Identification : KAMA efficiency determines whether conditions favor trend continuation or rotational movement, influencing stop sensitivity.
Volatility-Responsive Scaling : ATR-based stop distance adjusts automatically as efficiency rises or falls.
Pattern-Weighted Adjustment : KNN compares recent price sequences to historical analogs, applying confidence-based bias to stop positioning.
Adaptive Stop Smoothing : Long and short stop levels are smoothed using KAMA logic to maintain structural stability while remaining responsive.
Directional Trailing Enforcement : Stops advance only in the direction of the prevailing regime, preserving invalidation structure.
Gradient Distance Visualization : Gradient fills reflect the relative distance between price and the active stop.
Controlled Interaction Markers : Diamond markers highlight meaningful stop interactions, filtered through cooldown logic to reduce clustering.
Together, these elements form a continuously adapting trailing stop system rather than a fixed exit mechanism.
Interpretation
Adaptive ML Trailing Stop should be interpreted as a dynamic risk envelope:
Long Stop (Green) : Acts as the downside invalidation level during bullish regimes, tightening as efficiency improves.
Short Stop (Red) : Serves as the upside invalidation level during bearish regimes, adjusting width based on efficiency and volatility.
Trend State Changes : Regime flips occur only after confirmed stop breaches, filtering temporary price spikes.
Gradient Depth : Deeper gradient penetration indicates increased extension from the stop rather than imminent reversal.
Pattern Influence : KNN weighting affects stop behavior only when historical agreement is strong and remains neutral otherwise.
Distance, efficiency, and context outweigh isolated price interactions.
Signal Logic & Visual Cues
Adaptive ML Trailing Stop presents two primary visual signals:
Trend Transition Circles : Display when price crosses the opposing trailing stop, confirming a regime change rather than anticipating one.
Stop Interaction Diamonds : Indicate controlled contact with the active stop, subject to cooldown filtering to avoid excessive signals.
Alert generation is limited to confirmed trend transitions to maintain clarity.
Strategy Integration
Adaptive ML Trailing Stop fits within trend-following and risk-managed trading approaches:
Dynamic Risk Framing : Use adaptive stops as evolving invalidation levels instead of fixed exits.
Directional Alignment : Base execution on confirmed regime state rather than speculative reversals.
Efficiency-Based Tolerance : Allow greater price fluctuation during inefficient movement while enforcing tighter control during clean trends.
Pattern-Guided Refinement : Let KNN influence adjust sensitivity without overriding core structure.
Multi-Timeframe Context : Apply higher-timeframe efficiency states to inform lower-timeframe stop responsiveness.
Technical Implementation Details
Core Engine : KAMA-based efficiency measurement with adaptive smoothing
Volatility Model : ATR-derived stop distance scaled by regime
Machine Learning Layer : Distance-weighted KNN with confidence modulation
Visualization : Directional trailing stops with layered gradient fills
Signal Logic : Regime-based transitions and controlled interaction markers
Performance Profile : Optimized for real-time chart execution
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Tight adaptive trailing for short-term momentum control
15 - 60 min : Structured intraday trend supervision
4H - Daily : Higher-timeframe regime monitoring
Suggested Baseline Configuration:
KAMA Length : 20
Fast/Slow Periods : 15 / 50
ATR Period : 21
Base ATR Multiplier : 2.5
Adaptive Strength : 1.0
KNN Neighbors : 7
KNN Influence : 0.2
These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance.
Parameter Calibration Notes
Use the following adjustments to refine behavior without altering the core logic:
Excessive chop or overreaction : Increase KAMA Length, Slow Period, and ATR Period to reinforce regime filtering.
Stops feel overly permissive : Reduce the Base ATR Multiplier to tighten invalidation boundaries.
Frequent false regime shifts : Increase KNN Neighbors to demand stronger historical agreement.
Delayed adaptation : Decrease KAMA Length and Fast Period to improve responsiveness during regime change.
Adjustments should be incremental and evaluated over multiple market cycles rather than isolated sessions.
Performance Characteristics
High Effectiveness:
Markets exhibiting sustained directional efficiency
Instruments with recurring structural behavior
Trend-oriented, risk-managed strategies
Reduced Effectiveness:
Highly erratic or event-driven price action
Illiquid markets with unreliable volatility readings
Integration Guidelines
Confluence : Combine with BOSWaves structure or trend indicators
Discipline : Follow adaptive stop behavior rather than forcing exits
Risk Framing : Treat stops as adaptive boundaries, not forecasts
Regime Awareness : Always interpret stop behavior within efficiency context
Disclaimer
Adaptive ML Trailing Stop is a professional-grade adaptive risk and regime management tool. It does not forecast price movement and does not guarantee profitability. Results depend on market conditions, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates structure, volatility, and contextual risk management.
ADX Volatility Waves [BOSWaves]ADX Volatility Waves - Trend-Weighted Volatility Mapping with State-Based Wave Transitions
Overview
ADX Volatility Waves is a regime-aware volatility framework designed to map statistically significant price extremes through adaptive wave structures driven by trend strength.
Rather than treating volatility as a static dispersion metric, this indicator conditions all volatility expansion, contraction, and zone placement on ADX-derived trend intensity. Price behavior is interpreted through wave-like transitions between balance, expansion, and exhaustion states rather than isolated band interactions.
The result is a dynamic, gradient-based wave system that visually encodes volatility cycles and regime shifts in real time, allowing traders to contextualize price movement within trend-weighted volatility waves.
Price is evaluated not by static thresholds, but by its position and progression within adaptive volatility waves shaped by directional strength.
Conceptual Framework
ADX Volatility Waves is built on the premise that volatility unfolds in waves, not straight lines.
Traditional volatility tools identify dispersion but fail to account for how volatility behaves differently across trend regimes. By embedding ADX directly into volatility construction, this indicator ensures that volatility waves expand during strong directional phases and compress during weak or transitioning regimes.
Three guiding principles define the framework:
Volatility must be conditioned on trend strength
Extremes occur within zones, not at lines
Signals should emerge from completed wave transitions, not instantaneous touches
This reframes analysis from reactive mean-reversion toward regime-aware wave interpretation.
Theoretical Foundation
The indicator fuses directional movement theory with statistical volatility modeling.
Bollinger-derived dispersion provides the structural base, while ADX normalization controls the amplitude of volatility waves. As ADX increases, volatility waves widen and deepen; as ADX weakens, waves compress and tighten around equilibrium.
From this foundation, extended upper and lower wave zones are constructed and smoothed to represent statistically significant expansion and contraction phases.
At its core are three interacting systems:
ADX-Controlled Volatility Engine : Standard deviation is dynamically scaled using normalized ADX values, producing trend-weighted volatility waves.
Wave Zone Construction : Smoothed volatility boundaries are offset and expanded to form upper and lower wave zones, defining overextension and compression regions.
State-Based Wave Transition Logic : Signals occur only after price completes a full wave cycle: expansion into an extreme wave zone followed by a confirmed return to equilibrium.
This structure ensures that signals reflect completed volatility waves, not transient noise.
How It Works
ADX Volatility Waves processes price action through layered wave mechanics:
Trend-Weighted Volatility Calculation : Volatility boundaries are dynamically adjusted using ADX influence, allowing wave amplitude to scale with trend strength.
Structural Smoothing : Volatility boundaries are smoothed to stabilize wave geometry and reduce short-term distortions.
Wave Offset & Expansion : Upper and lower wave zones are positioned beyond equilibrium and expanded proportionally to volatility range, forming clearly defined expansion waves.
Gradient Wave Depth Mapping : Each wave zone is subdivided into multiple gradient layers, visually encoding increasing extremity as price moves deeper into a wave.
Wave State Tracking & Cooldown Control : The system tracks prior wave occupancy, enforces neutral stabilization periods, and applies cooldowns to prevent overlapping wave signals.
Compression Detection : Volatility width monitoring identifies compression phases, highlighting conditions where new volatility waves are likely to form.
Together, these processes create a continuous, adaptive wave map of volatility behavior.
Interpretation
ADX Volatility Waves reframes market reading around volatility cycles:
Upper Volatility Waves (Red Gradient) : Represent upside expansion phases. Deeper wave penetration indicates increased overextension relative to trend-adjusted volatility.
Lower Volatility Waves (Green Gradient) : Represent downside expansion phases. Sustained presence signals pressure, while exits toward balance suggest wave completion.
Equilibrium Zone : The neutral region between volatility waves. Confirmed re-entry into this zone marks the completion of a wave cycle and forms the basis for BUY and SELL signals.
Regime Context via ADX : Strong ADX regimes widen waves, reducing premature reversal signals. Weak ADX regimes compress waves, increasing sensitivity to reversion.
Wave progression and completion matter more than single-bar interactions.
Signal Logic & Visual Cues
ADX Volatility Waves produces single-entry BUY and SELL labels as its visual cues, plotted only when price first enters a volatility wave zone after the defined cooldown period.
Buy Signal (Bottom Zone Entry) : A BUY label appears when price enters the lower volatility wave (oversold zone). This highlights potential expansion into undervalued extremes, providing visual context for trend assessment rather than a guaranteed execution trigger.
Sell Signal (Top Zone Entry) : A SELL label appears when price enters the upper volatility wave (overbought zone). This marks potential overextension into upper volatility extremes, serving as a contextual indicator of trend stress.
All labels respect cooldown tracking to prevent clustering. Alerts are tied directly to these zone-entry signals, and a separate alert monitors volatility squeezes for awareness of compression periods.
Strategy Integration
ADX Volatility Waves integrates cleanly into volatility-aware trading frameworks:
Wave Context Mapping : Use wave depth to assess expansion and exhaustion risk rather than forcing immediate entries.
Transition-Based Execution : Prioritize BUY and SELL signals formed after confirmed wave completion.
Trend-Regime Filtering : In strong ADX regimes, treat waves as continuation pressure. In weak regimes, favor completed wave reversions.
Volatility Cycle Awareness : Monitor compression phases to anticipate the emergence of new volatility waves.
Multi-Timeframe Alignment : Apply higher-timeframe ADX regimes to contextualize lower-timeframe wave behavior.
Technical Implementation Details
Core Engine : ADX-normalized volatility expansion
Wave System : Smoothed, offset, expanded volatility waves
Visualization : Multi-layer gradient wave zones
Signal Logic : State-based wave transitions with cooldown enforcement
Alerts : Wave entry, wave completion, volatility compression
Performance Profile : Lightweight, real-time optimized overlay
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Short-term volatility waves and intraday transitions
15 - 60 min : Structured intraday wave cycles
4H - Daily : Macro volatility regimes and expansion phases
Suggested Baseline Configuration:
BB Length : 20
BB StdDev : 1.5
ADX Length : 14
ADX Influence : 0.8
Wave Offset : 1.0
Wave Width : 1.0
Neutral Confirmation : 5 bars
These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance.
Performance Characteristics
High Effectiveness:
Markets exhibiting rhythmic volatility expansion and contraction
Assets with responsive ADX regime behavior
Reduced Effectiveness:
Erratic, news-driven price action
Illiquid markets with distorted volatility metrics
Integration Guidelines
Confluence : Combine with BOSWaves structure or trend tools
Discipline : Respect wave completion and cooldown logic
Risk Framing : Interpret wave depth probabilistically, not predictively
Regime Awareness : Always contextualize waves within ADX strength
Disclaimer
ADX Volatility Waves is a professional-grade volatility and regime-mapping tool. It does not predict price and does not guarantee profitability. Performance depends on market conditions, parameter calibration, and disciplined execution. BOSWaves recommends using this indicator as part of a comprehensive analytical framework incorporating trend, volatility, and structural context.
VWAP-Anchored MACD [BOSWaves]VWAP-Anchored MACD - Volume-Weighted Momentum Mapping With Zero-Line Filtering
Overview
The VWAP-Anchored MACD delivers a refined momentum model built on volume-weighted price rather than raw closes, giving you a more grounded view of trend strength during sessions, weeks, or months.
Instead of tracking two EMAs of price like a standard MACD, this tool reconstructs the MACD engine using anchored VWAP as the core input. The result is a momentum structure that reacts to real liquidity flow, filters out weak crossovers near the zero line, and visualizes acceleration shifts with clear, high-contrast gradients.
This indicator acts as a precise momentum map that adapts in real time. You see how weighted price is accelerating, where valid crossovers form, and when trend conviction is strong enough to justify execution.
It uses gradient line coloring to show bullish or bearish momentum, histogram shading to highlight energy shifts, cross dots to mark valid crossovers, optional buy/sell diamonds for execution cues, and candle coloring to display trend strength at a glance.
Theoretical Foundation
Traditional MACD compares the difference between two exponential moving averages of price.
This variant replaces price with anchored VWAP, making the calculation sensitive to actual traded volume across your chosen period (Session, Week, or Month).
Three principles drive the logic:
Anchored VWAP Momentum : Price is weighted by volume and aggregated across the selected anchor. The fast and slow VWAP-EMAs then expose how liquidity-corrected momentum is expanding or contracting.
Zero-Line Distance Filtering : Crossover signals that occur too close to the zero line are removed. This eliminates the common MACD problem of generating weak, directionless signals in choppy phases.
Directional Visualization : MACD line, signal line, histogram, candle colors, and optional diamond markers all react to shifts in VWAP-momentum, giving you a clean structural read on market pressure.
Anchoring VWAP to session, weekly, or monthly resets creates a systematic framework for tracking how capital flow is driving momentum throughout each trading cycle.
How It Works
The core engine processes momentum through several mapped layers:
VWAP Aggregation : Price × volume is accumulated until the anchor resets. This creates a continuous, liquidity-corrected VWAP curve.
MACD Construction : Fast and slow VWAP-EMAs define the MACD line, while a smoothed signal line identifies edges where momentum shifts.
Zero-Line Distance Filter : MACD and signal must both exceed a threshold distance from zero for a crossover to count as valid. This prevents fake crossovers during compression.
Visual Momentum Layers : It uses gradient line coloring to show bullish or bearish momentum, histogram shading to highlight energy shifts, cross dots to mark valid crossovers, optional buy/sell diamonds for execution cues, and candle coloring to display trend strength at a glance.
This layered structure ensures you always know whether momentum is strengthening, fading, or transitioning.
Interpretation
You get a clean, structural understanding of VWAP-based momentum:
Bullish Phases : MACD > Signal, histogram expands, candles turn bullish, and crossovers occur above the threshold.
Bearish Phases : MACD < Signal, histogram drives lower, candles shift bearish, and downward crossovers trigger below the threshold.
Neutral/Compression : Both lines remain near the zero boundary, histogram flattens, and signals are suppressed to avoid noise.
This creates a more disciplined version of MACD momentum reading - less noise, more conviction, and better alignment with liquidity.
Strategy Integration
Trend Continuation : Use VWAP-MACD crossovers that occur far from the zero line as higher-conviction entries.
Zero-Line Rejection : Watch for histogram contractions near zero to anticipate flattening momentum and potential reversal setups.
Session/Week/Month Anchors : Session anchor works best for intraday flows. Weekly or monthly anchor structures create cleaner macro momentum reads for swing trading.
Signal-Only Execution : Optional buy/sell diamonds give you direct points to trigger trades without overanalyzing the chart.
This indicator slots cleanly into any momentum-following system and offers higher signal quality than classic MACD variants due to the volume-weighted core.
Technical Implementation Details
VWAP Reset Logic : Session (D), Week (W), or Month (M)
Dynamic Fast/Slow VWAP EMAs : Fully configurable lengths, smoothing and anchor settings
MACD/Signal Line Framework : Traditional structure with volume-anchored input
Zero-Line Filtering : Adjustable threshold for structural confirmation
Dual Visualization Layers : MACD body + histogram + crosses + candle coloring
Optimized Performance : Lightweight, fast rendering across all timeframes
Optimal Application Parameters
Timeframes:
1- 15 min : Short-term momentum scalping and rapid trend shifts
30- 240 min : Balanced momentum mapping with clear structural filtering
Daily : Macro VWAP regime identification
Suggested Configuration:
Fast Length : 12
Slow Length : 26
Signal Length : 9
Zero Threshold : 200 - 500 depending on asset range
These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance.
Performance Characteristics
High Effectiveness:
Assets with strong intraday or session-based volume cycles
Markets where volume-weighted momentum leads price swings
Trend environments with strong acceleration
Reduced Effectiveness:
Ultra-choppy markets hugging the VWAP axis
Sessions with abnormally low volume
Ranges where MACD naturally compresses
Disclaimer
The VWAP-Anchored MACD is a structural momentum tool designed to enhance directional clarity - not a guaranteed predictor. Performance depends on market regime, volatility, and disciplined execution. Use it alongside broader trend, volume, and structural analysis for optimal results.
