Arnaud Legoux Moving Average for Smoother, Lower-Lag Signals

Summary

The Arnaud Legoux Moving Average (ALMA) is described as a price-smoothing method that applies Gaussian weights across a rolling window. Its offset parameter shifts the weight distribution and adjusts the balance between smoothness and responsiveness, while sigma controls the shape of the weights. The document says this design aims to reduce small fluctuations while limiting the lag associated with conventional averages. It also suggests substituting ALMA into indicators that rely on moving averages, including RSI, MACD, and Stochastic calculations.

A sample implementation illustrates calculating and normalizing the weighted price average, with example settings for window, sigma, and offset. The text also characterizes the method as a zero-phase filtering approach, but does not provide comparative tests, trading rules, or performance evidence. Parameter tuning is encouraged without a stated selection procedure, so results may depend on the instrument, timeframe, and evaluation method. Traders would need to test responsiveness, noise reduction, and any added indicator signals on their own data, accounting for overfitting and execution costs.

Key ideas

• ALMA uses Gaussian weights over a rolling price window to calculate a smoothed average.
• The offset shifts the weighting profile, while sigma affects its shape.
• The method is presented as reducing noise while limiting moving-average lag.
• ALMA can replace conventional averages inside other technical indicators.
• The document gives example settings but no comparative performance evidence or parameter-selection method.

Tags

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