KNN Volume Normality Trend Hunter

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Indices · Timeframes: H1, D1

Thesis

Market trends are most exploitable when price returns deviate from a normal distribution (fat tails) and are confirmed by high-volume efficiency (Volume to Absolute Return Ratio). By using KNN to classify these volume-price relationships within a specific historical price corridor (Probable High/Low), we can identify high-conviction entries that represent non-random market movements.

Components

• Simple Moving Average (SMA) (regime) — Provides the primary trend filter; trades are only taken in the direction of the long-term baseline.
• ZigZag Color (Classic) (direction) — Identifies the current market structure leg to ensure alignment with immediate swing momentum.
• Volume SuperTrend AI (Expo) (entry) — Uses KNN classification to identify high-probability trend entries based on volume-weighted historical patterns.
• Volume Price Confirmation Indicator (VPCI) (exit) — Signals the end of a trend when volume no longer supports price movement, triggering an exit.
• Jarque-Bera Test (JB) (risk) — Determines position sizing by measuring the deviation from normality; higher JB values suggest non-random, trending behavior.
• Volume to Absolute Return Ratio (confirmation) — Confirms that price movement is backed by sufficient 'effort' (volume) relative to the return magnitude.
• Probable High Low (volatility_filter) — Acts as a hardcoded 'fair value' corridor. Trading only occurs when price is within these specific boundaries to avoid overextension.

Known failure conditions

• Price stays outside the hardcoded Probable High/Low levels for extended periods, rendering the system inactive.
• High frequency of ZigZag repainting leads to whipsaws in low-volatility environments.
• The KNN algorithm fails to generalize if market microstructure shifts (e.g., move to dark pools).

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