Multi-Timeframe Trend Filtering with Three Smoothed Moving Averages

Summary

This trend-following method uses three moving averages calculated on progressively higher timeframes. A crossover between the first two provides an entry signal, while the direction of the third average acts as confirmation. Dynamic smoothing is described as a way to display higher-timeframe averages more smoothly on a lower-timeframe chart. The parameters allow different average types and lengths, as well as configurable timeframes; the source also includes equity-based or fixed contract sizing and exits when the first two averages cross back.

The document says the stacked filters can reduce trades and warns that strict confirmation may produce few signals. It recommends testing average lengths for each market and suggests adding other filters or automatic parameter selection. A BTC/USDT futures backtest window and settings are listed, but no performance figures are given. The description claims improved reliability, yet the material provides no comparative evidence, and the strategy may miss opportunities when trends change before its averages confirm them.

Key ideas

• The method combines moving averages from three timeframes to align entry signals with broader trend direction.
• A crossover of the first two averages is confirmed by the direction of the third average.
• Dynamic smoothing is intended to make higher-timeframe averages more readable on a lower-timeframe chart.
• The source supports multiple average types, configurable periods, position sizing choices, and crossover exits.
• Strict filtering may reduce signal frequency, and the published backtest settings contain no reported performance results.

Tags

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