KNN-Volumatic Liquidity Oscillator
Family: hybrid · Regime: trending · Complexity: high · Asset classes: FX, Equities, Crypto · Timeframes: 15m, 1h, 4h
Thesis
Institutional order flow creates significant price-volume clusters (Volumatic S/R) that act as liquidity magnets. By classifying the market regime using a K-Nearest Neighbors approach on volume-weighted prices, we can identify when these clusters are likely to act as springboards rather than break-points. The edge exists because price often 'retests' high-volume nodes before a trend continuation, and confirming this with ADL slope ensures we are trading with, not against, the accumulation cycle.
Components
- Volume SuperTrend AI (Expo) (regime) — Filters the global trend using KNN classification to ensure we only trade in the direction of the dominant volume-weighted regime.
- Fisher-based Scalping Indicator (direction) — Identifies cyclical turning points and local momentum direction within the broader regime.
- Volumatic Support/Resistance Levels [BigBeluga] (entry) — Provides the precise entry trigger based on price interaction with high-volume liquidity zones.
- Relative Strength Index (RSI) (exit) — Detects overextended conditions to trigger exits before momentum exhaustion leads to reversal.
- RVOL (Relative Volume) (risk) — Dynamic risk adjustment; reduces position size during low-liquidity periods and calibrates stop-loss distance.
- Accumulation/Distribution Line (confirmation) — Confirms that the price action is backed by sustained volume flow (slope analysis).
Known failure conditions
- Persistent low-volume environments where RVOL remains below 0.8 for extended periods, causing the Volumatic zones to narrow and whip.
- Failure of the KNN algorithm to converge on a stable classification during 'flash' regime changes.
- Breakdown of the correlation between ADL and price, indicating 'fake' volume or wash trading.
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