Bitcoin Impulse Pullbacks with Multi-Timeframe Trend and ATR Exits

Summary

This Bitcoin strategy seeks entries on short-term pullbacks following a strong move. It detects impulses from the change in a smoothed primary-timeframe price relative to ATR, with an additional filter comparing current ATR to its recent average. A higher-timeframe EMA supplies a broad directional filter, while entry-timeframe prices are checked against levels derived from the impulse range and a decay factor. The described defaults use hourly impulse data, three-minute entry data, and a four-hour trend filter.

Stops use entry-timeframe ATR, while target levels are placed at decaying fractions of the estimated oscillation amplitude. The script also defines a partial-profit order and caps entries per impulse, with a chart heatmap indicating qualifying signals. The page describes these design choices but provides no backtest results or evidence that the approach is profitable. The displayed code has implementation details that merit scrutiny: its entry counter reset condition is tied to the first bar, and its impulse levels depend on values that are only populated during qualifying impulse bars. These behaviors may affect when signals and exits occur.

Key ideas

• The strategy identifies impulses by comparing a smoothed price change with primary-timeframe ATR.
• It combines primary, entry, and trend timeframes to filter pullback entries.
• A higher-timeframe EMA is used to define bullish and bearish market bias.
• Stop distances use entry-timeframe ATR, while targets decay from an impulse-based amplitude.
• The document describes the rules but supplies no performance evidence, and code behavior should be checked before relying on the signals.

Tags

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