NY Liquidity Momentum Hybrid
Family: hybrid · Regime: trending · Complexity: high · Asset classes: FX, Indices, Equities · Timeframes: M15, H1, D1
Thesis
Market edge exists when institutional liquidity sweeps (Daily Sweep) are followed by high-momentum price action (Order Blocks). By filtering these entries with volume validation (WatchTower) and ensuring alignment with medium-term trend (Ichimoku) and volatility (Bollinger Bands), we can capture the 'Meat of the Move' during the highest liquidity window (NY Open) while using dynamic momentum (LinReg) to maximize exit efficiency.
Components
- Bollinger Bands (regime) — Acts as a regime filter; long trades are only permitted when price is in the lower half or expanding from the mean, preventing buying at volatility extremes.
- The Daily Sweep (direction) — Provides the core directional bias based on institutional liquidity grabs and daily market structure.
- Sonarlab - Order Blocks (entry) — Identifies the specific high-momentum demand/supply zones within the Daily Sweep bias for precise entry execution.
- Linear Regression (LINREG) (exit) — Provides a dynamic trend-exhaustion exit signal based on the slope of price momentum.
- Average True Range (ATR) (risk) — Determines volatility-adjusted stop-loss placement and position sizing.
- Ichimoku Kinko Hyo (confirmation) — Provides secondary confirmation that price is trading above/below the cloud to ensure alignment with medium-term trend.
- KandleKings™ WatchTower v1.0 (volatility_filter) — Filters out low-momentum entries by requiring volume-backed VWAP breakouts and RSI momentum.
Known failure conditions
- Continuous 'walking of the bands' where price trends without returning to Order Blocks.
- Prolonged low-volume environments where WatchTower volume triggers never fire.
- Series of stop-outs during 'expanding triangle' formations where sweeps occur in both directions.
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.