Three-SMA Trend-Following Strategy with a KAMA Filter

Summary

This strategy uses 16-, 36-, and 72-period simple moving averages to identify directional alignment: the shortest average crossing above the other two supports a long signal, while crossing below them supports a short signal. A Kaufman Adaptive Moving Average (KAMA) is presented as an additional filter intended to screen out trades when conditions are unclear. The source also includes a 100-period trend average and closes positions when price and moving-average conditions indicate the trade should stop.

The document frames the method as a straightforward, automatable trend-following approach, particularly for volatile crypto markets. It supplies parameter values and published backtest settings for BTC/USDT futures, but reports no performance statistics, so the settings alone do not demonstrate effectiveness. The prose and source differ on the KAMA condition: the prose describes trading during a linear phase, while the code excludes that phase. The strategy also lacks a conventional stop-loss, and the document warns that ranging markets can generate repeated false signals. Parameter tuning, added filters, and position management are suggested, but not validated.

Key ideas

• The strategy uses three simple moving averages to define bullish or bearish alignment.
• A KAMA condition is intended to filter the moving-average signals, though the prose and source describe that condition differently.
• The source includes a longer trend average and closes positions based on moving-average and price conditions.
• Published backtest settings identify BTC/USDT futures, but the document gives no performance results.
• Ranging markets, lagging signals, and the lack of a conventional stop-loss are stated limitations.

Tags

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