Neural-Volume Session Extension Trader

Family: hybrid · Regime: trending · Complexity: medium · Asset classes: Equities, Indices · Timeframes: 5m, 15m

Thesis

Intraday price discovery is most efficient when the session's initial balance (Opening Range) aligns with the long-term institutional volume trend (200-Day). By using a KNN algorithm to filter volume-weighted momentum signals, we can identify high-probability entries that exploit the tendency of markets to trend toward extension levels when session 'fair value' (OR Mid) is defended. The edge relies on the structural persistence of institutional flow over retail noise.

Components

• Trepidity Opening Range with Extensions (regime) — Establishes the session's 'fair value' (OR Mid) and volatility boundaries. Prevents over-extending into late-session exhaustion.
• 200-Day Trend Background (Smoothed Volume Gradient) (direction) — Provides the institutional directional bias; we only trade intraday signals that align with the high-timeframe volume-weighted trend.
• Volume SuperTrend AI (Expo) (entry) — Uses KNN classification to filter VWMA-based trend signals, ensuring entry occurs only when price/volume patterns resemble historical success.
• RSI Area (Histogram) (exit) — Identifies local momentum exhaustion to capture profits before mean reversion to the OR Mid occurs.
• SuperTrend (risk) — Provides a dynamic, ATR-based trailing stop that adjusts for intraday volatility.

Known failure conditions

• Price frequently crosses the OR_Mid without reaching Extension_1, indicating a lack of session conviction.
• Volume Gradient remains high while price moves counter-trend, suggesting a large-scale distribution/accumulation phase that the KNN cannot classify.
• The 200-day slope remains flat (within the neutral zone) for extended periods, starving the system of signals.

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).