LSTM-Regime Gator Trend Navigator
Family: trend_following · Regime: trending · Complexity: high · Asset classes: Forex, Crypto, Indices · Timeframes: 15m, 1h, 4h
Thesis
The strategy hypothesizes that trend-following success is not about identifying direction alone, but about entering during the specific transition from market 'Awakening' to 'Eating' (Gator) while ensuring price movement is statistically leading its mean (Mean Error). By using an AI-optimized regime filter (ML SuperTrend) to scale ATR factors, we can theoretically filter out noise that standard trend indicators catch, while using market structure (Boxline) to define risk in a non-arbitrary way.
Components
- ML SuperTrend (Ultimate) - Auto-Optimized AI with LSTM (regime) — Acts as the primary regime filter, using LSTM-based volatility clusters to define whether the market is in a 'tradable' trend state versus noise.
- Ichimoku Kinko Hyo (direction) — Provides a secondary directional filter; trades are only taken when price is on the correct side of the Kumo (cloud) to ensure structural alignment.
- UTBot Alerts (entry) — Generates the specific entry trigger based on price crossing a highly reactive ATR-based trailing stop.
- Custom 004 Liquidity Map with Drawings (exit) — Identifies 20-period exhaustion points (HH/LL) to serve as take-profit targets based on local liquidity pools.
- Boxline (Dynamic Range Breakout) (risk) — Used exclusively for risk management; the dynamic boxes define the current price 'containment' zone, providing a hard floor/ceiling for stop losses.
- Mean Error (ME) (confirmation) — Confirms that the current price movement is leading the historical estimate (positive bias for longs, negative for shorts), reducing entries into exhausted trends.
- Gator Oscillator (volatility_filter) — Filters for 'Eating' phases (high intensity) to ensure the trend has sufficient momentum to overcome the costs of entry.
Known failure conditions
- ML SuperTrend fails to converge or produces oscillating regime signals in low-liquidity environments.
- Boxline boundaries repaint excessively during high-impact news, leading to 'phantom' stop-loss levels.
- Mean Error stays near zero for extended periods, indicating a lack of directional bias despite price movement.
Explore the full interactive blueprint, parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine.