Lorentzian Mode-Regime Multi-Filter

Family: hybrid · Regime: trending · Complexity: high · Asset classes: FX, Indices, Commodities · Timeframes: H1, M15

Thesis

Market trends are most reliable when localized ML pattern recognition (Lorentzian) aligns with a statistical consensus of recent market states (Mode) and institutional volume flows (CMF). By using session extremes as risk anchors, the strategy bets on the structural integrity of daily liquidity ranges.

Components

• Mode of String Array (Compatibility Test) (regime) — Filters out idiosyncratic signals by requiring the most frequent recent market state (Bullish/Bearish/Chop) to match the intended trade direction.
• HiLo Activator (Gann Style) (direction) — Provides the primary trend bias by tracking price relative to lagged high/low moving averages.
• Lorentzian Classification (entry) — Uses non-Euclidean distance to identify historical price patterns similar to the current setup, acting as the primary entry trigger.
• UT Bot Alerts (MQL5) (exit) — An ATR-based trailing stop that reacts quickly to momentum shifts for time-sensitive exits.
• Session Range (High/Low/Mid) (risk) — Uses institutional liquidity boundaries (Session Highs/Lows) to define stop-loss levels and risk-adjusted position sizing.
• Chaikin Money Flow (NNFX Version) (confirmation) — Ensures the price move is backed by volume-weighted accumulation/distribution.
• Bollinger Bands (Standard) (volatility_filter) — Filters entries during periods of extreme volatility (bands expansion) or stagnation (squeeze).

Known failure conditions

• Price stays pinned to the Session Midpoint for extended periods, causing stop-outs on minor fluctuations.
• Lorentzian KNN classification accuracy drops below 50% in backtests, indicating the feature set is irrelevant to the current asset.
• CMF stays near zero regardless of price movement, indicating a lack of volume-driven participation.

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.