Direct Answer
Roofing Filter is a cycle & signal processing measure used to estimate dominant cycles or separate trend/cycle components with signal-processing methods. The production page must define the exact calculation convention before interpreting higher or lower readings, because platform and provider implementations can differ.
Roofing Filter: Formula, Meaning, Signals, Examples and How to Use It
How Is Roofing Filter Calculated?
The Roofing Filter is a John Ehlers band-pass construction that chains a high-pass filter and a Super Smoother. The high-pass step removes the trend and very long cycles, and the smoother removes high-frequency noise, leaving mid-length cycles.
Formula: Roofing = SuperSmoother(HighPass(price, long cutoff, for example 48), short cutoff, for example 10)
Inputs:
- Close prices
- High-pass cutoff period (commonly 48 bars)
- Super Smoother cutoff period (commonly 10 bars)
Reading it. The output swings around zero. Positive values mean price is above its trend-removed baseline within the passband, negative values below. It is mostly used as a cleaned input for other oscillators such as stochastics rather than read on its own.
Limits. The two cutoff periods are choices that decide which cycles survive, and a wrong choice leaves out the cycle that matters. Both filters are recursive, so the output depends on the start of the data and lags real turns.
Indicator overview
| Attribute | Value |
|---|---|
| Category | Cycle & Signal Processing |
| Asset classes | Stocks, ETFs, Futures, Forex, Crypto |
| Output type | Oscillator/line |
| Parameters | Varies by implementation; document exact settings on use |
What is Roofing Filter?
Roofing Filter is a cycle & signal processing measure used to estimate dominant cycles or separate trend/cycle components with signal-processing methods. The production page must define the exact calculation convention before interpreting higher or lower readings, because platform and provider implementations can differ.
For Swoopr, the canonical profile separates definition, calculation, interpretation and decision use. That keeps readers from collapsing a descriptive metric into a trading strategy. An indicator can describe trend, momentum, volatility, participation, positioning or risk without providing a complete entry, exit, sizing or portfolio decision.
What does it measure?
Roofing Filter measures specific market properties in the Cycle & Signal Processing domain. The practical value is repeatability: the same inputs and formula can be applied across a defined dataset, allowing users to compare readings through time or across instruments when the calculation convention is held constant.
How to read Roofing Filter
Interpretation depends on the market property being measured. A descriptive reading is not automatically predictive. The same numerical state can lead to different outcomes in a strong trend, quiet range, event-driven gap or illiquid market. Always combine with context from price structure, volume, and broader market regime before drawing conclusions.
Strengths
- Chaining a high-pass and a Super Smoother yields a band-pass output with a defined passband between the two cutoff periods.
- Removing trend before smoothing keeps the output centered on zero, so it can feed oscillator calculations without a drifting baseline.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- The output keeps the units of price and its amplitude scales with volatility, so any threshold on it would need normalization.
- The filter shape cuts off gradually, so cycles just outside the two cutoff periods are partly present rather than fully removed.
- Correlated indicators can create false confidence by repeating the same underlying information.
Editorial and risk note
This page is educational content, not individualized investment advice. It should not imply guaranteed prediction, accuracy or outperformance. Where multiple valid definitions exist, Swoopr states the alternatives and the reason for selecting its primary convention.
Frequently Asked Questions
Is Roofing Filter a buy or sell signal?
No. The output swings around zero. The two cutoff periods are choices that decide which cycles survive, and a wrong choice leaves out the cycle that matters. A trade built on it still needs a trigger, an invalidation rule, a position size and realistic execution assumptions.
What is the best setting for Roofing Filter?
Roofing Filter takes these inputs: close prices; high-pass cutoff period (commonly 48 bars); super Smoother cutoff period (commonly 10 bars). There is no universal best setting for the tunable ones, so test them across instruments, regimes and out-of-sample periods and favor stable parameter regions over one historical winner.
Can Roofing Filter be used by itself?
The two cutoff periods are choices that decide which cycles survive, and a wrong choice leaves out the cycle that matters. Both filters are recursive, so the output depends on the start of the data and lags real turns. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Roofing Filter work on every timeframe?
The calculation (Roofing = SuperSmoother(HighPass(price, long cutoff, for example 48), short cutoff, for example 10)) can be run on any bar size, but behavior is not portable: a reading on a five-minute chart describes a very different market window from the same reading on a daily chart. Validate the exact timeframe before relying on it.
Why does Roofing Filter give false signals?
The output keeps the units of price and its amplitude scales with volatility, so any threshold on it would need normalization. The filter shape cuts off gradually, so cycles just outside the two cutoff periods are partly present rather than fully removed. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Roofing Filter be backtested?
Rebuild Roofing Filter from close prices using point-in-time data, write the rules before testing, include delisted securities where relevant, model realistic fills and costs, reserve a validation sample and compare with a simpler baseline.