Overlap Studies
MESA Adaptive Moving Average (MAMA)
MESA Adaptive Moving Average
Deep Dive
Everything You Need to Know
Under the Hood
How It Works
MAMA is an advanced adaptive moving average developed by John Ehlers using his MESA (Maximum Entropy Spectrum Analysis) algorithm to detect dominant market cycles. It outputs two lines: MAMA (faster) and FAMA (Following Adaptive Moving Average). The fast_limit (default 0.5) and slow_limit (default 0.05) parameters control the adaptation range - MAMA adjusts its period based on detected cycle length using Hilbert Transform techniques. The indicator automatically speeds up and slows down based on measured market periodicity, making it highly responsive to changing cycle regimes.
In Practice
How Traders Use It
Advanced cryptocurrency traders use MAMA/FAMA crossovers as adaptive trend signals - buy when MAMA crosses above FAMA, sell when crossing below. The dual-line system provides faster signals than traditional moving average crossovers because both lines adapt to market cycles. MAMA excels in crypto markets with varying cycle lengths, automatically adjusting from fast day-trading signals to slower position-holding periods. It works particularly well when combined with Ehlers' other cycle indicators (HT_DCPERIOD, HT_TRENDMODE) for regime detection. Algorithmic traders favor MAMA for automated systems requiring cycle-aware trend following without manual optimization.
Highlights
MAMA at a Glance
- Adaptive moving average using MESA cycle detection
- Outputs MAMA (fast) and FAMA (following) lines
- Automatically adjusts to dominant market cycles
- Buy on MAMA/FAMA crossover up, sell on crossover down
- fast_limit (0.5) and slow_limit (0.05) control adaptation
- Uses Hilbert Transform for cycle measurement
- Developed by John Ehlers
- Best for varying cycle regime markets
- Combine with HT cycle indicators
- Popular among algorithmic and systematic traders
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FAQ
Frequently Asked Questions
What is the MESA Adaptive Moving Average (MAMA) indicator?
MESA Adaptive Moving Average
How does MAMA work?
MAMA is an advanced adaptive moving average developed by John Ehlers using his MESA (Maximum Entropy Spectrum Analysis) algorithm to detect dominant market cycles. It outputs two lines: MAMA (faster) and FAMA (Following Adaptive Moving Average). The fast_limit (default 0.5) and slow_limit (default 0.05) parameters control the adaptation range - MAMA adjusts its period based on detected cycle length using Hilbert Transform techniques. The indicator automatically speeds up and slows down based on measured market periodicity, making it highly responsive to changing cycle regimes.
How do traders use MAMA in their strategies?
Advanced cryptocurrency traders use MAMA/FAMA crossovers as adaptive trend signals - buy when MAMA crosses above FAMA, sell when crossing below. The dual-line system provides faster signals than traditional moving average crossovers because both lines adapt to market cycles. MAMA excels in crypto markets with varying cycle lengths, automatically adjusting from fast day-trading signals to slower position-holding periods. It works particularly well when combined with Ehlers' other cycle indicators (HT_DCPERIOD, HT_TRENDMODE) for regime detection. Algorithmic traders favor MAMA for automated systems requiring cycle-aware trend following without manual optimization.
What are the key points to know about MAMA?
Adaptive moving average using MESA cycle detection Outputs MAMA (fast) and FAMA (following) lines Automatically adjusts to dominant market cycles Buy on MAMA/FAMA crossover up, sell on crossover down fast_limit (0.5) and slow_limit (0.05) control adaptation Uses Hilbert Transform for cycle measurement Developed by John Ehlers Best for varying cycle regime markets Combine with HT cycle indicators Popular among algorithmic and systematic traders
Can I use MAMA with Cryptorobot.ai?
Yes. MESA Adaptive Moving Average (MAMA) is available as a built-in indicator in Cryptorobot.ai. You can add it to any automated strategy using the no-code strategy builder, backtest it against historical data, and deploy it live on supported exchanges.