Profitable MAMA & FAMA Crossover

Introduction
The MESA Adaptive Moving Average (MAMA) was originally presented by John F. Ehlers. By design, it is a special kind of Exponential Moving Average with self-adjusting alpha. Its adaptation is based on the rate change of phase as measured by the Homodyne Discriminator and the alpha parameter is allowed to range between a maximum and minimum value (Fast Limit and Slow Limit).

Key Point: Ehlers suggested the maximum value to be 0.5 and the minimum to be 0.05.
The variable alpha is computed as the Fast Limit divided by the phase rate of change. If the phase rate of change is large, the variable alpha is bounded at the SlowLimit. Then, this alpha is used to compute MAMA and FAMA (Following Adaptive Moving Average).

Should we rely on Ehlers' suggestions if we want to achieve the best result with MAMA & FAMA crossover system?
Well, he is a good specialist and widely recognized author, I respect him, but the answer is no and you can see results on the chart.

What is our goal?
We want to find the best configuration for MAMA & FAMA Crossover. To achieve that we need to analyze the MAMA's alpha parameter or, more specific, the bounds for this parameter, Fast and Slow Limits.

What is this tool?
This tool is a performance optimizer that uses decision tree-based algorithm under the hood to find the most profitable settings for the MAMA & FAMA Crossover. It analyzes a bunch of different Fast Limits (between 0.01 to 0.8 with step of 0.1) and Slow Limits (between 0.01 to 0.6 with step of 0.1) and backtests each combination across the entire history of an instrument. If the more profitable parameters were found, the indicator will switch its values to the found ones immediately.

So, instead of manually selecting and testing parameters just apply this indicator to your chart and
relax - the algorithm will find the best parameters for you

Alerts
It has a special alert that notifies when the more profitable settings were detected.

NOTE: It does not change what has already been plotted.
NOTE 2: This is not a strategy, but an algorithmic optimizer.

Reference: https://www.mesasoftware.com/papers/MAMA.pdf

MAMA & FAMA Crossover can be found here:

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版本注释:

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