LSTM-Float Structural Turnover Strategy

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: 15m, 1H, 4H

Thesis

Market reversals occur when a full turnover of the float is achieved (volume exhaustion) near structural supply/demand pivots. By using an LSTM-optimized regime filter to ensure the macro-trend is stable and the Gator oscillator to confirm active volatility, an edge can be found by entering on micro-candle patterns that signal the resumption of momentum at these structural nodes.

Components

• ML SuperTrend (Ultimate) - Auto-Optimized AI with LSTM (regime) — Defines the macro-regime; the LSTM/K-Means++ layers filter out noise that standard SuperTrend signals would fail in.
• Float Indicator (direction) — Identifies market turnover; confirms that sufficient volume has circulated to potentially shift the trend direction.
• Pattern Recognition Master (entry) — Provides the high-probability micro-trigger based on classical Japanese candlestick geometry.
• Relative Strength Index (with timeframe) (exit) — Measures momentum exhaustion to time exits before a full trend reversal occurs.
• Supply & Demand Zones (NNFX) (risk) — Uses structural pivots to define logical stop-loss levels and ensure the entry occurs at a favorable price area.
• Gator Oscillator (Standard) (volatility_filter) — Ensures the strategy only executes during 'eating' phases (expanding volatility) to avoid sideways chop.

Known failure conditions

• The ML LSTM weights fail to adapt to a structural change in market volatility (regime shift).
• The asset's circulating float is significantly different from the 'Float' lookback, leading to mistimed turnover signals.
• Price action becomes 'compressed' where S&D zones overlap continuously, leading to stop-loss hunts.

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