ETH Momentum Breakouts with Regime Filters and Adaptive Trade Management

Summary

This ETH-focused strategy seeks breakouts after volatility compression, using a broad set of filters to qualify direction and market conditions. Its design includes higher-timeframe trend and regime checks, EMA distance, optional ADX, volatility and volume conditions, momentum, candle quality, and RSI extreme filters. Squeeze detection based on Bollinger Bands and Keltner Channels identifies a recent compression window; a breakout must clear a price margin tied to ATR.

Trade management uses ATR-based stops and targets, partial profit-taking, breakeven protection, trailing stops, and an optional runner mode. Adaptive settings classify conditions as hot, normal, or cold using volatility rank and distance from a trend EMA, then scale trade parameters. The script also exposes cooldown, stagnation, and time-in-trade controls, with optional crypto fundamental inputs. It is parameter-rich and tuned in the document for a two-hour chart. The supplied excerpt includes settings and a dashboard but not enough complete logic or independent results to establish profitability; its settings and referenced historical observations should be tested against fees, slippage, and out-of-sample data.

Key ideas

• The strategy looks for breakouts shortly after a Bollinger and Keltner squeeze, with an ATR-based clearance margin.
• Higher-timeframe trend, volatility regime, EMA distance, volume, momentum, and RSI filters can qualify entries.
• ATR-based stops, partial targets, breakeven rules, and trailing exits manage open positions.
• Hot, normal, and cold classifications adapt trade parameters using volatility rank and trend distance.
• The excerpt does not provide complete performance evidence, and its many tunable filters require robust testing.

Tags

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