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Velocity Hybrid Oscillator (VHO)
🚀 VELOCITY HYBRID OSCILLATOR [VHO]
The Velocity Hybrid Oscillator (VHO), engineered by gunebak4n, is a dual-momentum normalization and trend velocity detection framework designed to isolate high-probability cyclical turning points and structural trend regimes.
VHO is built on the principle that true market velocity cannot be captured by a single momentum lookback or absolute price changes alone. By structurally merging the trend-following smoothing of the Price Momentum Oscillator (PMO) with the reactive cyclicality of the Relative Strength Index (RSI), and processing both through a rolling Min-Max normalization matrix, the system projects multi-dimensional momentum fields onto a fixed boundary.
Unlike conventional oscillators that frequently pin at extremes or trigger premature counter-trend signals during strong expansions, VHO normalizes relative strength and price velocity over a wide structural lookback, anchoring the final hybrid signal to a highly scannable, noise-filtered trend baseline.
💡 CORE DESIGN PRINCIPLE
🧭 Multi-Dimensional Momentum Convergence
VHO interprets market momentum not as an isolated price derivative, but as a convergence of absolute velocity (PMO) and internal strength velocity (RSI). True trend shifts emerge when these two fields align within a normalized range.
🧬 Absolute Velocity–Internal Strength–Smoothing Model
The system evaluates three interacting structural layers:
• Absolute Velocity: Double-smoothed Price Momentum Oscillator tracking rate of change.
• Internal Strength: Relative Strength Index tracking gains versus losses.
• Min-Max Normalization: Rescaling both metrics to a dynamic 144-period lookback.
The mathematical aggregation of these layers defines the final hybrid trend line.
💡 KEY FEATURES
📊 Double-Smoothed Absolute Velocity (PMO)
The system extracts clean, absolute price velocity by calculating a custom Price Momentum Oscillator. This process applies two sequential exponential moving averages to custom daily rate-of-change inputs, effectively filtering out high-frequency market noise before normalization.
🧪 Cyclical Internal Strength Layer (RSI)
To capture real-time overbought and oversold conditions without lagging the underlying price discovery, the system integrates a classical relative strength layer, subsequently smoothed by a simple moving average to align its frequency with the PMO.
📦 Dynamic Min-Max Normalization Matrix
To prevent standard indicator drift and pinning, both the raw PMO and smoothed RSI values are dynamically rescaled between 0 and 100 based on a fixed rolling lookback. This anchors all historical data points to identical relative boundaries.
📈 Velocity Hybrid Line Aggregation
The normalized absolute velocity and normalized internal strength are synthesized into a single "Sharp Line," which is then subjected to an exponential smoothing filter to generate the final, high-conviction "Hybrid Line."
🧠 Asymmetric Color & Directional Logic
The main signal line features an adaptive, four-state color architecture that reacts instantly to directional changes relative to the 50-midline pivot, shifting hue based on whether the velocity is expansionary or decaying.
🎨 Structural Divergence Detection Engine
VHO automatically maps multi-factor divergence signals across structural price action. High-probability continuation or reversal points are highlighted by identifying localized asymmetries where price makes new extremes while the Hybrid Line prints decaying peaks or troughs.
🔬 MATHEMATICAL STRUCTURE
Price Rate of Change (ROC):
ROC = ((Close - Close[1]) / 1) * 100
Double-Smoothed PMO:
PMO = EMA(EMA(ROC, Length1), Length2)
PMO_Raw = (PMO + PMO_Signal) / 2
RSI Internal Strength:
RSI = ta.rsi(Source, Length)
RSI_MaRaw = SMA(RSI, RSI_MA_Length)
Min-Max Normalization Function (f_normalize):
Min = Lowest(Source, Length)
Max = Highest(Source, Length)
f_normalize = 100 * (Source - Min) / max(Max - Min, 0.000001)
Hybrid Line Aggregation:
SharpLine = (f_normalize(PMO_Raw, n) + f_normalize(RSI_MaRaw, n)) / 2
HybridLine = EMA(SharpLine, k)
Divergence Signal Matrix:
BearishDiv = ta.rising(High, 5) and ta.falling(HybridLine, 5) and HybridLine > 70
BullishDiv = ta.falling(Low, 5) and ta.rising(HybridLine, 5) and HybridLine < 30
This structure ensures that cyclical overextended states are measured against a standardized historical baseline, rather than floating nominal boundaries.
🛠️ USAGE FRAMEWORK
1. Midline Pivot Evaluation
Treat the 50 level as the structural axis of equilibrium. Hybrid Line values sustained above 50 dictate a dominant bullish regime, while values below 50 confirm bearish structural control.
2. Directional Velocity Scaling
Monitor the four-state line coloration. A bright cyan (#00ffbb) or bright red (#ff5252) signal line denotes maximum acceleration in trend direction, while darker tones indicate velocity decay or potential consolidation.
3. Symmetrical Overextended Extremes
Utilize the 70 and 30 dashed reference levels to identify standard overbought and oversold thresholds. The dotted 90 and 10 parameters act as absolute extreme volatility caps.
4. Multi-Factor Divergence Validation
When the system prints red (bearish) or green (bullish) circles on the Hybrid Line, cross-reference these divergence zones with major support/resistance levels to time high-probability counter-trend reversals.
5. Regime Background Filtering
Leverage the optional "Trend Background" fill to maintain clear macro-bias context, allowing short-term hybrid fluctuations to be scaled within the overarching directional environment.
⚙️ SYSTEM CHARACTERISTICS
• Dual-engine absolute and internal velocity integration
• Fixed-boundary rolling Min-Max normalization matrix
• Four-state direction-and-regime sensitive line coloring
• Automatic high-probability divergence mapping
• Multi-layered zone fill for enhanced visual scannability
• Fully parameterized smoothing filters to match any operational asset class
📌 CREDIT
Velocity Hybrid Oscillator (VHO) is developed by gunebak4n as a structural momentum normalization and trend velocity detection framework for TradingView.
The system is designed for identifying statistically significant cyclical shifts emerging from multi-factor momentum compression, rather than isolated price-action breakouts.
⚠️ DISCLAIMER
VHO is a probabilistic analytical model. It does not predict market outcomes or guarantee trading performance. All signals must be interpreted within a disciplined risk management framework and validated with broader market context.
The Velocity Hybrid Oscillator (VHO), engineered by gunebak4n, is a dual-momentum normalization and trend velocity detection framework designed to isolate high-probability cyclical turning points and structural trend regimes.
VHO is built on the principle that true market velocity cannot be captured by a single momentum lookback or absolute price changes alone. By structurally merging the trend-following smoothing of the Price Momentum Oscillator (PMO) with the reactive cyclicality of the Relative Strength Index (RSI), and processing both through a rolling Min-Max normalization matrix, the system projects multi-dimensional momentum fields onto a fixed boundary.
Unlike conventional oscillators that frequently pin at extremes or trigger premature counter-trend signals during strong expansions, VHO normalizes relative strength and price velocity over a wide structural lookback, anchoring the final hybrid signal to a highly scannable, noise-filtered trend baseline.
💡 CORE DESIGN PRINCIPLE
🧭 Multi-Dimensional Momentum Convergence
VHO interprets market momentum not as an isolated price derivative, but as a convergence of absolute velocity (PMO) and internal strength velocity (RSI). True trend shifts emerge when these two fields align within a normalized range.
🧬 Absolute Velocity–Internal Strength–Smoothing Model
The system evaluates three interacting structural layers:
• Absolute Velocity: Double-smoothed Price Momentum Oscillator tracking rate of change.
• Internal Strength: Relative Strength Index tracking gains versus losses.
• Min-Max Normalization: Rescaling both metrics to a dynamic 144-period lookback.
The mathematical aggregation of these layers defines the final hybrid trend line.
💡 KEY FEATURES
📊 Double-Smoothed Absolute Velocity (PMO)
The system extracts clean, absolute price velocity by calculating a custom Price Momentum Oscillator. This process applies two sequential exponential moving averages to custom daily rate-of-change inputs, effectively filtering out high-frequency market noise before normalization.
🧪 Cyclical Internal Strength Layer (RSI)
To capture real-time overbought and oversold conditions without lagging the underlying price discovery, the system integrates a classical relative strength layer, subsequently smoothed by a simple moving average to align its frequency with the PMO.
📦 Dynamic Min-Max Normalization Matrix
To prevent standard indicator drift and pinning, both the raw PMO and smoothed RSI values are dynamically rescaled between 0 and 100 based on a fixed rolling lookback. This anchors all historical data points to identical relative boundaries.
📈 Velocity Hybrid Line Aggregation
The normalized absolute velocity and normalized internal strength are synthesized into a single "Sharp Line," which is then subjected to an exponential smoothing filter to generate the final, high-conviction "Hybrid Line."
🧠 Asymmetric Color & Directional Logic
The main signal line features an adaptive, four-state color architecture that reacts instantly to directional changes relative to the 50-midline pivot, shifting hue based on whether the velocity is expansionary or decaying.
🎨 Structural Divergence Detection Engine
VHO automatically maps multi-factor divergence signals across structural price action. High-probability continuation or reversal points are highlighted by identifying localized asymmetries where price makes new extremes while the Hybrid Line prints decaying peaks or troughs.
🔬 MATHEMATICAL STRUCTURE
Price Rate of Change (ROC):
ROC = ((Close - Close[1]) / 1) * 100
Double-Smoothed PMO:
PMO = EMA(EMA(ROC, Length1), Length2)
PMO_Raw = (PMO + PMO_Signal) / 2
RSI Internal Strength:
RSI = ta.rsi(Source, Length)
RSI_MaRaw = SMA(RSI, RSI_MA_Length)
Min-Max Normalization Function (f_normalize):
Min = Lowest(Source, Length)
Max = Highest(Source, Length)
f_normalize = 100 * (Source - Min) / max(Max - Min, 0.000001)
Hybrid Line Aggregation:
SharpLine = (f_normalize(PMO_Raw, n) + f_normalize(RSI_MaRaw, n)) / 2
HybridLine = EMA(SharpLine, k)
Divergence Signal Matrix:
BearishDiv = ta.rising(High, 5) and ta.falling(HybridLine, 5) and HybridLine > 70
BullishDiv = ta.falling(Low, 5) and ta.rising(HybridLine, 5) and HybridLine < 30
This structure ensures that cyclical overextended states are measured against a standardized historical baseline, rather than floating nominal boundaries.
🛠️ USAGE FRAMEWORK
1. Midline Pivot Evaluation
Treat the 50 level as the structural axis of equilibrium. Hybrid Line values sustained above 50 dictate a dominant bullish regime, while values below 50 confirm bearish structural control.
2. Directional Velocity Scaling
Monitor the four-state line coloration. A bright cyan (#00ffbb) or bright red (#ff5252) signal line denotes maximum acceleration in trend direction, while darker tones indicate velocity decay or potential consolidation.
3. Symmetrical Overextended Extremes
Utilize the 70 and 30 dashed reference levels to identify standard overbought and oversold thresholds. The dotted 90 and 10 parameters act as absolute extreme volatility caps.
4. Multi-Factor Divergence Validation
When the system prints red (bearish) or green (bullish) circles on the Hybrid Line, cross-reference these divergence zones with major support/resistance levels to time high-probability counter-trend reversals.
5. Regime Background Filtering
Leverage the optional "Trend Background" fill to maintain clear macro-bias context, allowing short-term hybrid fluctuations to be scaled within the overarching directional environment.
⚙️ SYSTEM CHARACTERISTICS
• Dual-engine absolute and internal velocity integration
• Fixed-boundary rolling Min-Max normalization matrix
• Four-state direction-and-regime sensitive line coloring
• Automatic high-probability divergence mapping
• Multi-layered zone fill for enhanced visual scannability
• Fully parameterized smoothing filters to match any operational asset class
📌 CREDIT
Velocity Hybrid Oscillator (VHO) is developed by gunebak4n as a structural momentum normalization and trend velocity detection framework for TradingView.
The system is designed for identifying statistically significant cyclical shifts emerging from multi-factor momentum compression, rather than isolated price-action breakouts.
⚠️ DISCLAIMER
VHO is a probabilistic analytical model. It does not predict market outcomes or guarantee trading performance. All signals must be interpreted within a disciplined risk management framework and validated with broader market context.
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Penafian
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Skrip sumber terbuka
Dalam semangat TradingView sebenar, pencipta skrip ini telah menjadikannya sumber terbuka, jadi pedagang boleh menilai dan mengesahkan kefungsiannya. Terima kasih kepada penulis! Walaupuan anda boleh menggunakan secara percuma, ingat bahawa penerbitan semula kod ini tertakluk kepada Peraturan Dalaman.
Penafian
Maklumat dan penerbitan adalah tidak bertujuan, dan tidak membentuk, nasihat atau cadangan kewangan, pelaburan, dagangan atau jenis lain yang diberikan atau disahkan oleh TradingView. Baca lebih dalam Terma Penggunaan.