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.

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