Lorentzian-ICT Float Exhaustion Strategy

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: H1, H4, D1

Thesis

Market price action is non-linear and exhibits 'memory' that can be classified via Lorentzian distance. However, these ML signatures only become tradable when aligned with institutional order-flow shifts (ICT Mitigation Blocks) and confirmed by volume turnover (Float Trader). By filtering for momentum expansion (Gator) and targeting exogenous liquidity (Q-Levels), we exploit the gap between retail indicators and institutional price targets. This strategy assumes that price moves from one volume-clearing event to an institutional liquidity level, with ML providing the probabilistic bias.

Components

• Lorentzian Classification (regime) — Sets the macro-regime by finding historical price-action analogs using manifold learning, ensuring the strategy trades with the 'machine-learned' momentum.
• ICT Mitigation Block Scanner (direction) — Provides directional bias by identifying structural failures (mitigation blocks) where previous order flow is neutralized, indicating a high-probability reversal point.
• Float Trader Indicator (entry) — Triggers the entry when the cumulative volume between swings 'resets', signaling that the current float has turned over and a new price discovery phase is starting.
• Q-Levels V2.2 (exit) — Provides exogenous exit targets based on manual/institutional data (gamma walls, expected moves) that aren't visible in pure price-action charts.
• Williams Fractals (risk) — Used for hard-stop placement at the most recent swing extreme, acknowledging the 2-bar confirmation lag for risk calculation.
• Gator Oscillator (volatility_filter) — Acts as a volatility gate; prevents entries during 'sleeping' phases (contracted histograms) and ensures momentum is 'eating' (expanding) before committing capital.

Known failure conditions

• Price breaches the opposite Q-Level before the target is reached, suggesting a total structural shift.
• Lorentzian classification flips polarity more than 3 times in 10 bars (choppiness).
• Float trader fails to reset for extended periods, indicating a parabolic move where volume turnover logic breaks.

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.