Direct Answer
John Ehlers' MAMA uses the rate of change of phase, measured with a Hilbert transform, to adapt its smoothing speed. It is paired with FAMA, a slower line that follows MAMA, and the two form a crossover system. MAMA crossing above FAMA is read as an upturn and crossing below as a downturn.
MESA Adaptive Moving Average: Formula, Meaning, Signals, Examples and How to Use It
How Is MESA Adaptive Moving Average Calculated?
John Ehlers' MAMA uses the rate of change of phase, measured with a Hilbert transform, to adapt its smoothing speed. It is paired with FAMA, a slower line that follows MAMA, and the two form a crossover system.
Formula: MAMA(t) = alpha * Price + (1 - alpha) * MAMA(t-1); FAMA(t) = 0.5*alpha * MAMA + (1 - 0.5*alpha) * FAMA(t-1); alpha comes from the phase change, limited to between the slow and fast limits
Inputs:
- Median prices
- Fast limit (0.5 is a common default)
- Slow limit (0.05 is a common default)
- Hilbert transform phase calculation
Worked example. With an adaptive alpha of 0.3, a price of 102 and a prior MAMA of 100, the new MAMA value is 100.60. The inputs are illustrative numbers, not market data.
Reading it. MAMA crossing above FAMA is read as an upturn and crossing below as a downturn. Because MAMA adapts its speed, the two lines stay apart in trends and bunch together in ranges.
Limits. The phase calculation is complicated and sensitive to implementation details, so values differ between platforms. Adaptive logic can lag at the start of a new cycle.
What is MESA Adaptive Moving Average?
John Ehlers' MAMA uses the rate of change of phase, measured with a Hilbert transform, to adapt its smoothing speed. It is paired with FAMA, a slower line that follows MAMA, and the two form a crossover system.
How to use MESA Adaptive Moving Average
MAMA crossing above FAMA is read as an upturn and crossing below as a downturn. Because MAMA adapts its speed, the two lines stay apart in trends and bunch together in ranges. A reading alone is never a buy or sell signal; it needs context from price structure, volume, volatility and the broader market regime.
Strengths
- The two lines MAMA and FAMA are produced together, so a crossover rule needs no second set of parameters beyond the limits.
- The smoothing factor is bounded between a fast and a slow limit, so the line cannot become frozen or a copy of price.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- Phase measurements from a Hilbert transform need several bars of history before they stabilize, so early values are unreliable.
- The transform assumes a cycle component in the data, and a series without clear cycles produces erratic phase estimates.
- 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.
Frequently Asked Questions
Is MESA Adaptive Moving Average a buy or sell signal?
No. MAMA crossing above FAMA is read as an upturn and crossing below as a downturn. The phase calculation is complicated and sensitive to implementation details, so values differ between platforms. 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 MESA Adaptive Moving Average?
MESA Adaptive Moving Average is computed from median prices; fast limit (0.5 is a common default); slow limit (0.05 is a common default), so the choices that matter are the data convention and the window it is read over. Test those across instruments, regimes and out-of-sample periods instead of fitting one historical winner.
Can MESA Adaptive Moving Average be used by itself?
The phase calculation is complicated and sensitive to implementation details, so values differ between platforms. Adaptive logic can lag at the start of a new cycle. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does MESA Adaptive Moving Average work on every timeframe?
The calculation (MAMA(t) = alpha * Price + (1 - alpha) * MAMA(t-1); FAMA(t) = 0.5*alpha * MAMA + (1 - 0.5*alpha) * FAMA(t-1); alpha comes from the phase change, limited to between the slow and fast limits) 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 MESA Adaptive Moving Average give false signals?
Phase measurements from a Hilbert transform need several bars of history before they stabilize, so early values are unreliable. The transform assumes a cycle component in the data, and a series without clear cycles produces erratic phase estimates. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should MESA Adaptive Moving Average be backtested?
Rebuild MESA Adaptive Moving Average from median 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.