Volatility Adaptive Filtered Trend [SchizoQuant]
Volatility Adaptive Filtered Trend is an adaptive trend-regime indicator that adjusts its smoothing response according to both current volatility and directional price movement, then applies a displacement-weighted filter before constructing volatility-responsive breakout bands.
Instead of relying on a fixed moving-average speed, the indicator continuously estimates how active current market conditions are relative to their recent history. That activity measurement determines the smoothing coefficient used by the adaptive trend, allowing the baseline to respond faster during stronger movement and more slowly during quieter conditions.
🟣 ✦ How It Works
The indicator is built from four main stages:
Market Activity → Measures current ATR and directional price movement relative to their recent averages.
Adaptive Trend → Converts that activity measurement into a variable smoothing coefficient.
Filtered Trend → Applies additional EMA smoothing followed by a displacement-weighted filtering process.
Volatility Bands → Builds upper and lower thresholds around the filtered trend using the standard deviation of price returns.
The resulting bands define the bullish and bearish regime boundaries.
🟣 ✦ Adaptive Market Response
The first stage measures two forms of market activity.
ATR Factor compares current ATR with its recent ATR average.
Change Factor compares the absolute directional movement of price over the Volatility Length with its own recent average.
These two measurements are averaged into a Combined Factor and normalized to a range between 0 and 1.
Higher normalized values represent stronger current activity relative to recent conditions.
Lower normalized values represent quieter or less directional conditions.
🟣 ✦ Adaptive Trend
The normalized activity measurement is mapped between the user-defined Minimum Smoothing and Maximum Smoothing values.
Higher market activity moves the smoothing coefficient toward Maximum Smoothing, making the adaptive trend react more quickly to price.
Lower activity moves the coefficient toward Minimum Smoothing, producing a slower and more filtered response.
The adaptive trend is calculated recursively, allowing its effective response speed to change continuously as market conditions evolve.
🟣 ✦ EMA Stabilization
The adaptive trend is passed through an additional EMA smoothing stage.
This step reduces short-term variation in the adaptive baseline before the final filtering process is applied.
EMA Smoothing therefore provides an additional trade-off between responsiveness and stability.
Lower values preserve more of the adaptive trend's immediate movement.
Higher values create a smoother intermediate series.
🟣 ✦ Displacement-Weighted Filter
The EMA-smoothed adaptive trend is then processed through a displacement-weighted filter.
For each observation inside the Filter Length, the script compares the current sample with a sample located Momentum Length bars farther back.
The absolute difference between those observations becomes the weighting coefficient.
Larger historical displacement therefore gives that observation greater influence on the final filtered trend.
The weighted observations are combined into the final trend baseline.
If no usable displacement weights are available, a simple moving average of the EMA-smoothed adaptive trend is used as a fallback.
🟣 ✦ Volatility Bands
Band volatility is measured from the standard deviation of one-bar percentage returns.
The volatility measurement uses the previous completed value of return volatility when constructing the current bands.
The upper and lower thresholds are calculated independently around the filtered trend:
Upper Band = Filtered Trend + Upper Multiplier × Return Volatility × Filtered Trend
Lower Band = Filtered Trend - Lower Multiplier × Return Volatility × Filtered Trend
Because the upper and lower multipliers are independent, bullish and bearish regime sensitivity can be configured separately.
🟣 ✦ Persistent Regime Logic
A bullish condition occurs when the current price is above the upper volatility band.
A bearish condition occurs when the current price is below the lower volatility band.
The indicator maintains a persistent regime:
Price above Upper Band → Bullish regime
Price below Lower Band → Bearish regime
Price between the bands → Previous regime remains active
LONG and SHORT markers therefore appear only when the persistent regime changes rather than on every bar that remains outside a band.
🟣 ✦ Main Settings
Volatility Length controls the ATR and directional-price-movement measurements used to estimate current market activity.
Normalization Length determines how much historical data is used to establish the reference levels for ATR and directional movement.
Minimum Smoothing defines the slowest adaptive trend response.
Maximum Smoothing defines the fastest adaptive trend response.
EMA Smoothing controls the additional smoothing applied before the displacement-weighted filter.
Filter Length determines how many observations contribute to the final weighted trend.
Momentum Length determines the historical distance used when calculating each observation's displacement weight.
Band Volatility Length controls how much return history is used to estimate volatility around the filtered trend.
Upper Multiplier controls the distance of the bullish threshold above the filtered trend.
Lower Multiplier controls the distance of the bearish threshold below the filtered trend.
🟣 ✦ Design Purpose
Each component serves a separate role:
ATR activity → Measures current volatility intensity
Directional movement → Measures recent price displacement
Normalized activity → Determines adaptive smoothing speed
Adaptive smoothing → Dynamic trend response
EMA smoothing → Reduces short-term variation
Displacement weighting → Emphasizes observations associated with stronger movement
Return volatility → Determines adaptive band width
Persistent regime → Maintains the active bullish or bearish trend state
The main design characteristic is that trend responsiveness and regime sensitivity are controlled through different mechanisms.
The trend baseline adapts according to relative market activity, while the surrounding thresholds respond independently to return volatility.
This allows the centerline and the regime boundaries to adjust to changing market conditions without relying on the same volatility measurement for both tasks.
🟣 ✦ Limitations
Volatility Adaptive Filtered Trend is a trend-regime indicator and not a complete trading system. LONG and SHORT markers identify changes in the indicator's internal regime and do not imply guaranteed trading outcomes or future performance.
The adaptive smoothing mechanism depends on historical ATR and directional price movement. Rapid changes in market conditions can therefore alter the responsiveness of the trend baseline.
The displacement-weighted filter gives greater influence to observations associated with larger historical changes. During irregular or highly volatile conditions, this weighting can shift the filtered trend toward periods containing stronger displacement.
The volatility bands are based on historical return dispersion. Large recent price movements can widen the bands and increase the distance required for subsequent regime changes.
Like other band-based trend methods, the indicator may respond later to abrupt reversals because price must cross the opposite volatility threshold before the persistent regime changes.
Signals are evaluated on the active chart bar and are not restricted to confirmed candle closes. As a result, a regime condition or LONG/SHORT signal can change before the current candle closes.
The script does not use higher-timeframe requests or lookahead logic.
Instead of relying on a fixed moving-average speed, the indicator continuously estimates how active current market conditions are relative to their recent history. That activity measurement determines the smoothing coefficient used by the adaptive trend, allowing the baseline to respond faster during stronger movement and more slowly during quieter conditions.
🟣 ✦ How It Works
The indicator is built from four main stages:
Market Activity → Measures current ATR and directional price movement relative to their recent averages.
Adaptive Trend → Converts that activity measurement into a variable smoothing coefficient.
Filtered Trend → Applies additional EMA smoothing followed by a displacement-weighted filtering process.
Volatility Bands → Builds upper and lower thresholds around the filtered trend using the standard deviation of price returns.
The resulting bands define the bullish and bearish regime boundaries.
🟣 ✦ Adaptive Market Response
The first stage measures two forms of market activity.
ATR Factor compares current ATR with its recent ATR average.
Change Factor compares the absolute directional movement of price over the Volatility Length with its own recent average.
These two measurements are averaged into a Combined Factor and normalized to a range between 0 and 1.
Higher normalized values represent stronger current activity relative to recent conditions.
Lower normalized values represent quieter or less directional conditions.
🟣 ✦ Adaptive Trend
The normalized activity measurement is mapped between the user-defined Minimum Smoothing and Maximum Smoothing values.
Higher market activity moves the smoothing coefficient toward Maximum Smoothing, making the adaptive trend react more quickly to price.
Lower activity moves the coefficient toward Minimum Smoothing, producing a slower and more filtered response.
The adaptive trend is calculated recursively, allowing its effective response speed to change continuously as market conditions evolve.
🟣 ✦ EMA Stabilization
The adaptive trend is passed through an additional EMA smoothing stage.
This step reduces short-term variation in the adaptive baseline before the final filtering process is applied.
EMA Smoothing therefore provides an additional trade-off between responsiveness and stability.
Lower values preserve more of the adaptive trend's immediate movement.
Higher values create a smoother intermediate series.
🟣 ✦ Displacement-Weighted Filter
The EMA-smoothed adaptive trend is then processed through a displacement-weighted filter.
For each observation inside the Filter Length, the script compares the current sample with a sample located Momentum Length bars farther back.
The absolute difference between those observations becomes the weighting coefficient.
Larger historical displacement therefore gives that observation greater influence on the final filtered trend.
The weighted observations are combined into the final trend baseline.
If no usable displacement weights are available, a simple moving average of the EMA-smoothed adaptive trend is used as a fallback.
🟣 ✦ Volatility Bands
Band volatility is measured from the standard deviation of one-bar percentage returns.
The volatility measurement uses the previous completed value of return volatility when constructing the current bands.
The upper and lower thresholds are calculated independently around the filtered trend:
Upper Band = Filtered Trend + Upper Multiplier × Return Volatility × Filtered Trend
Lower Band = Filtered Trend - Lower Multiplier × Return Volatility × Filtered Trend
Because the upper and lower multipliers are independent, bullish and bearish regime sensitivity can be configured separately.
🟣 ✦ Persistent Regime Logic
A bullish condition occurs when the current price is above the upper volatility band.
A bearish condition occurs when the current price is below the lower volatility band.
The indicator maintains a persistent regime:
Price above Upper Band → Bullish regime
Price below Lower Band → Bearish regime
Price between the bands → Previous regime remains active
LONG and SHORT markers therefore appear only when the persistent regime changes rather than on every bar that remains outside a band.
🟣 ✦ Main Settings
Volatility Length controls the ATR and directional-price-movement measurements used to estimate current market activity.
Normalization Length determines how much historical data is used to establish the reference levels for ATR and directional movement.
Minimum Smoothing defines the slowest adaptive trend response.
Maximum Smoothing defines the fastest adaptive trend response.
EMA Smoothing controls the additional smoothing applied before the displacement-weighted filter.
Filter Length determines how many observations contribute to the final weighted trend.
Momentum Length determines the historical distance used when calculating each observation's displacement weight.
Band Volatility Length controls how much return history is used to estimate volatility around the filtered trend.
Upper Multiplier controls the distance of the bullish threshold above the filtered trend.
Lower Multiplier controls the distance of the bearish threshold below the filtered trend.
🟣 ✦ Design Purpose
Each component serves a separate role:
ATR activity → Measures current volatility intensity
Directional movement → Measures recent price displacement
Normalized activity → Determines adaptive smoothing speed
Adaptive smoothing → Dynamic trend response
EMA smoothing → Reduces short-term variation
Displacement weighting → Emphasizes observations associated with stronger movement
Return volatility → Determines adaptive band width
Persistent regime → Maintains the active bullish or bearish trend state
The main design characteristic is that trend responsiveness and regime sensitivity are controlled through different mechanisms.
The trend baseline adapts according to relative market activity, while the surrounding thresholds respond independently to return volatility.
This allows the centerline and the regime boundaries to adjust to changing market conditions without relying on the same volatility measurement for both tasks.
🟣 ✦ Limitations
Volatility Adaptive Filtered Trend is a trend-regime indicator and not a complete trading system. LONG and SHORT markers identify changes in the indicator's internal regime and do not imply guaranteed trading outcomes or future performance.
The adaptive smoothing mechanism depends on historical ATR and directional price movement. Rapid changes in market conditions can therefore alter the responsiveness of the trend baseline.
The displacement-weighted filter gives greater influence to observations associated with larger historical changes. During irregular or highly volatile conditions, this weighting can shift the filtered trend toward periods containing stronger displacement.
The volatility bands are based on historical return dispersion. Large recent price movements can widen the bands and increase the distance required for subsequent regime changes.
Like other band-based trend methods, the indicator may respond later to abrupt reversals because price must cross the opposite volatility threshold before the persistent regime changes.
Signals are evaluated on the active chart bar and are not restricted to confirmed candle closes. As a result, a regime condition or LONG/SHORT signal can change before the current candle closes.
The script does not use higher-timeframe requests or lookahead logic.
🟣 Profitable Crypto Strategies | Free Trial — schizoquant.com
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.
🟣 Profitable Crypto Strategies | Free Trial — schizoquant.com
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.