Mean Error Volatility-Trend Hybrid

Family: trend_following · Regime: trending · Complexity: medium · Asset classes: Forex, Equities, Indices · Timeframes: H1, H4

Thesis

The hypothesis is that persistent directional bias (Mean Error) coupled with a surge in relative volume (RVOL) identifies a shift from noise to a trend. By using a local polynomial filter (SGF) to confirm the trend's integrity and a volatility-adaptive trigger (UT Bot), we can enter momentum moves just as institutional participation peaks, exiting when double-smoothed momentum (TSI) begins to diverge.

Components

• Mean Error (ME) (regime) — Uses the bias of price against its mean to determine if the market is in a persistent trend (high ME) or mean-reverting (low ME) regime.
• CCI Arrows (direction) — Captures high-velocity momentum shifts as CCI crosses the zero midline to align with the trend.
• UT Bot Alerts (MQL5) (entry) — Provides the final execution trigger based on ATR-trailing stop breakouts.
• TSI Convergence Divergence (TSI_CD) (exit) — Signals the exhaustion of momentum through histogram contraction/crossover for timely exits.
• Roshaneforde Pivot Levels (risk) — Provides structural support/resistance based on reversal patterns for SL placement.
• Relative Volume (RVOL) (confirmation) — Ensures entries occur during periods of high participation to avoid low-liquidity whipsaws.
• Savitzky-Golay Filter (SGF) (volatility_filter) — Smooths price noise to ensure the entry signal occurs on a statistically significant local trend.

Known failure conditions

• If the ME remains near 0 during extended trending periods due to error cancellation.
• High-frequency oscillation of the TSI_CD histogram in low-volatility environments.
• Failure of Rosheneforde levels to appear during parabolic moves without reversals.

Explore the full interactive blueprint with parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine (free download).