Adaptive SuperTrend with Volatility Regime Filtering

Summary

This strategy pairs an ATR-based SuperTrend with volatility regime classification to generate trend-following entries. It estimates high, medium, and low volatility bands from a rolling ATR range, then conditions direction changes on the assigned regime: long entries are associated with the lowest volatility category, while short entries require the highest. ATR-based stop and profit levels are also specified, with the profit target set relative to the stop distance.

The document presents default settings and a BTC-USDT futures backtest configuration lasting one week, but gives no returns, trade statistics, or comparative evidence. Although it describes the clustering as K-means, the included source assigns regimes using fixed fractions of the rolling ATR range rather than a visible K-means fit. The authors identify parameter sensitivity, whipsaws in ranging markets, sudden reversals, and computational overhead as concerns. The short test setup cannot demonstrate effectiveness across markets or timeframes.

Key ideas

• The strategy uses ATR bands to calculate a SuperTrend direction and detect possible trend shifts.
• Volatility regimes are estimated from the rolling range of ATR and used to filter long and short entries.
• ATR multiples define stop-loss and take-profit distances, with targets set farther from entry than stops.
• The source's regime assignments use range thresholds, despite the description's reference to K-means clustering.
• The brief backtest configuration provides no performance evidence, and ranging conditions may cause repeated signals.

Tags

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