GARCH-PVT Momentum Statistical Hybrid

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, FX, Crypto · Timeframes: 1H, 4H, D1

Thesis

Market trends are only sustainable when accompanied by volume accumulation (PVT) and manageable volatility clustering (GARCH). By entering when momentum (TEMA) aligns with long-term direction (LSMA) while price is not at a statistical extreme (Z-Score), we capture the 'meat' of the move. Risk is managed not by arbitrary pip counts, but by the conditional probability of price variance (CV).

Components

• Price Volume Trend (PVT) (regime) — Acts as the regime filter to ensure price movements are supported by volume accumulation/distribution rather than low-liquidity noise.
• Supported plot Input Show Last (direction) — Provides a 'recency filter' ensuring the trend is visible in the most immediate price action window.
• Frankenstein Ultimate Pro - ATM Logic (entry) — The core execution engine using TEMA for timing and LSMA for trend alignment, filtered by ATR-based expansion.
• Fisher-based Scalping Indicator (exit) — Identifies cyclical exhaustion using a Fisher-transformed oscillator to time exits before mean reversion occurs.
• Conditional Volatility (CV) (risk) — Uses GARCH(1,1) to estimate volatility clustering, providing a dynamic basis for stop-loss distance and position sizing.
• Z-Score (volatility_filter) — Standardizes price distance from the mean to prevent entering at statistical extremes (blow-off tops).

Known failure conditions

• The GARCH model fails to converge or underestimates risk during 'black swan' discontinuous price gaps.
• PVT and Price diverge persistently, indicating a breakdown in the volume-price relationship.
• Market enters a tight bracket where Z-Score oscillates rapidly near zero, triggering multiple false entries.

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