MESA Adaptive Moving Average Signals from Estimated Market Cycles

Summary

This strategy applies John Ehlers’s MESA adaptive moving average approach to price data. It smooths and detrends price, derives in-phase and quadrature components, and uses their relationship to estimate a cycle period and phase. Those estimates adjust the smoothing parameter for the MAMA and FAMA curves; their relative position generates long or short entries. The listed parameters are fast and slow limits, and the published setup uses BTC/USDT futures data at daily resolution with hourly base data.

The document explains the indicator’s construction and discusses potential strengths, including adaptation to changing conditions. It also warns that distorted prices, bounded period estimates, and oscillation near turning points can create misleading signals, while higher volatility may impair adaptation. It provides no performance results to substantiate its claims of robustness or stability. Suggested improvements include smoother estimation, filtering with other information, attention to slippage, and risk controls; these are proposals rather than evaluated results.

Key ideas

• The strategy smooths and detrends price before estimating its cyclical components.
• In-phase and quadrature components inform a period estimate and adaptive MAMA and FAMA curves.
• The relative position of MAMA and FAMA determines directional entries.
• The document identifies false signals and sensitivity during unusual or volatile conditions as risks.
• No measured strategy performance is supplied.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.