Drift-Adaptive Mean Reversion Trend Strategy

Family: trend_following · Regime: trending · Complexity: high · Asset classes: FX, Equities, Commodities · Timeframes: H1, H4

Thesis

Market trends exhibit persistent 'drift' that can be statistically distinguished from random noise using Variance Ratios (ADM). By aligning with this drift via the HiLo Activator and Alligator, and entering on mean-reversion pullbacks to the Bollinger SMA, we capture the trend resumption at a superior risk-reward ratio compared to traditional breakout methods.

Components

• HiLo Activator 02 (regime) — Defines the primary trend regime based on staircase support/resistance levels.
• Bill Williams Alligator (direction) — Confirms directional momentum and ensures the 'Alligator is eating' (moving averages are fanned out) to avoid late-stage trend exhaustion.
• Bollinger Bands (entry) — Provides a mean-reversion entry trigger within the established trend, entering on a 'touch of the middle band' to improve R:R.
• ATR Fib (exit) — Uses session-specific volatility to project Fibonacci extension targets, providing structural take-profit levels.
• FakeCandle (risk) — Used to calculate the stop-loss level based on the synthetic candle structure to filter out sub-pip noise.
• Asset Drift Model (ADM) (volatility_filter) — Acts as a statistical gatekeeper to ensure the asset is exhibiting non-random drift before trend-following filters are applied.

Known failure conditions

• ADM stays near zero or indicates mean-reversion (stationary) while trend indicators signal entries.
• Price remains pinned to the Bollinger Band outer levels without returning to the SMA, indicating a 'blow-off top' regime.
• ATR Fib levels are tighter than the average daily range, causing frequent premature exits.

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