Smoothed Moving Average (SMMA)

Overview

The Smoothed Moving Average operates on a unique principle where historical data is never completely discarded but rather given progressively smaller weights over time. The first SMMA value is calculated as a simple moving average, but subsequent values use the recursive formula: SMMA = (SMMA(prev) × (N-1) + Current Price) / N. This approach creates a moving average that gives recent prices equal weighting to historic ones initially, then gradually diminishes the impact of older data points without ever removing them entirely from the calculation.

What makes SMMA particularly valuable is its extreme smoothness and minimal lag compared to simple moving averages of the same period. By incorporating all available data with exponentially decreasing influence, it filters out short-term market noise more effectively than other moving averages. This characteristic makes SMMA ideal for identifying genuine trend changes and establishing dynamic support and resistance levels, as its slow reaction to price movements reduces false signals while maintaining responsiveness to significant market shifts.

Interpretation & Trading Signals

Example Usage

Code examples will be available once the Rust implementation is complete.