Recursive Fractal Volume Momentum

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

Thesis

Market trends are most exploitable when price breakouts from local structure (Fractals) are accompanied by positive volume flow (A/D) and supported by a noise-reduced, fast-adapting trend regime (RLS). By using a recursive filter, we minimize the lag found in traditional moving averages, allowing for faster entry into structural expansions.

Components

• Recursive Least Squares Adaptive Filter (RLS) (regime) — Provides a high-speed, noise-reduced trend regime filter that adapts faster than standard moving averages to market shifts.
• Accumulation/Distribution (A/D) Index (direction) — Ensures the price move is backed by volume-weighted buying/selling pressure (accumulation) rather than low-liquidity spikes.
• Williams Fractals (entry) — Identifies local structural support and resistance levels for breakout entry triggers.
• Bollinger bands / Connectable [Azullian] (exit) — Detects volatility-driven exhaustion points to exit the trade before mean reversion occurs.
• Frankenstein Ultimate Pro - ATM Logic (risk) — Combines TEMA and LSMA to filter out low-momentum environments and provides the primary stop-loss logic.
• Average True Range (ATR) (volatility_filter) — Filters out periods of extreme volatility (likely news-driven) and extreme low volatility (illiquid).

Known failure conditions

• The RLS P-matrix becomes poorly conditioned, leading to erratic regime signals.
• Market enters a low-volatility 'stair-stepping' regime where price grinds past fractals without momentum.
• Volume (A/D) diverges consistently from price for more than 3 consecutive trend cycles.

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