Moving Average Candle Momentum Regression with SuperTrend Filter

Summary

This strategy smooths open, high, low, and close prices into moving-average candles, then measures the difference between the smoothed close and a moving average of that close. A linear regression of this momentum series helps classify its direction and strength. Long entries are triggered by selected shifts in regression state, subject to a SuperTrend direction filter; exits use momentum color-state transitions. Moving-average types and lengths, percentile thresholds, and Bollinger-style deviation settings are configurable. The implementation also includes an optional VIX Fix path, though it is disabled by default.

The document presents the method and a short published test window on BTC/USDT futures, but gives no performance statistics or evidence that the approach is profitable. It flags complex parameter tuning, tradeoffs among indicators, and relatively infrequent signals. The described logic is long-oriented, and its filtering and signal rules are not independently validated in the text. Readers would need to examine implementation details and test across broader periods and market conditions before drawing conclusions.

Key ideas

• The method smooths OHLC prices into moving-average candles before calculating momentum.
• Linear regression of the smoothed-price difference is used to identify momentum changes.
• A SuperTrend direction check gates long entries, while momentum state changes provide exits.
• The strategy exposes several moving-average, lookback, percentile, and deviation parameters.
• The document gives no performance statistics and identifies parameter complexity and sparse signals as limitations.

Tags

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