Direct Answer
Kaufman Adaptive Moving Average is a trend measure used to describe trend direction, persistence, strength, or a smoothed price reference. The production page must define the exact calculation convention before interpreting higher or lower readings, because platform and provider implementations can differ.
Kaufman Adaptive Moving Average: Formula, Meaning, Signals, Examples and How to Use It
How Is Kaufman Adaptive Moving Average Calculated?
Perry Kaufman's KAMA changes its speed based on the efficiency ratio of recent price movement. It follows quickly when price moves in a straight line and slows down when price is choppy.
Formula: KAMA(t) = KAMA(t-1) + SC^2 * (Price - KAMA(t-1)), SC = ER * (fast - slow) + slow, fast = 2/(2+1), slow = 2/(30+1)
Inputs:
- Closing prices
- Efficiency ratio lookback (10 is common)
- Fastest smoothing period 2
- Slowest smoothing period 30
Worked example. With an efficiency ratio of 0.5, a prior KAMA of 100 and a price of 104, the new KAMA is 100.535. The inputs are illustrative numbers, not market data.
Reading it. In directional markets KAMA tracks price closely, and in noisy ranges it flattens. Price crossing KAMA, or KAMA changing slope, is read as a trend change.
Limits. Its behavior depends on the efficiency ratio window and the fast and slow limits. A sudden trend start from a flat period may be followed slowly at first because the ratio is still low.
Indicator overview
| Attribute | Value |
|---|---|
| Category | Trend |
| Asset classes | Stocks, ETFs, Indexes, Futures, Forex, Crypto, Commodities |
| Output type | Line/scalar |
| Parameters | Varies by implementation; document exact settings on use |
What is Kaufman Adaptive Moving Average?
Kaufman Adaptive Moving Average is a trend measure used to describe trend direction, persistence, strength, or a smoothed price reference. 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?
Kaufman Adaptive Moving Average measures specific market properties in the Trend 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 Kaufman Adaptive Moving Average
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
- The efficiency ratio compares net movement with total path, so speed is set by how directly price has moved, not by volatility alone.
- The smoothing constant is bounded by fast and slow limits, so the line always stays between a quick and a slow average.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- The efficiency ratio is computed from a fixed window, so its value jumps when a large single-bar move enters or leaves.
- Squaring the smoothing constant makes the response drop sharply when the ratio is low, so the line can look nearly flat in noisy periods.
- 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 Kaufman Adaptive Moving Average a buy or sell signal?
No. In directional markets KAMA tracks price closely, and in noisy ranges it flattens. Its behavior depends on the efficiency ratio window and the fast and slow limits. 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 Kaufman Adaptive Moving Average?
Kaufman Adaptive Moving Average takes these inputs: closing prices; efficiency ratio lookback (10 is common); fastest smoothing period 2. 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 Kaufman Adaptive Moving Average be used by itself?
Its behavior depends on the efficiency ratio window and the fast and slow limits. A sudden trend start from a flat period may be followed slowly at first because the ratio is still low. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Kaufman Adaptive Moving Average work on every timeframe?
The calculation (KAMA(t) = KAMA(t-1) + SC^2 * (Price - KAMA(t-1)), SC = ER * (fast - slow) + slow, fast = 2/(2+1), slow = 2/(30+1)) 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 Kaufman Adaptive Moving Average give false signals?
The efficiency ratio is computed from a fixed window, so its value jumps when a large single-bar move enters or leaves. Squaring the smoothing constant makes the response drop sharply when the ratio is low, so the line can look nearly flat in noisy periods. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Kaufman Adaptive Moving Average be backtested?
Rebuild Kaufman Adaptive Moving Average from closing 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.