Volume SuperTrend AI (Expo)

Category: trend, volume

Complexity-enhanced SuperTrend that uses Volume-Weighted Moving Averages for its baseline and applies a K-Nearest Neighbors (KNN) classification algorithm to categorize trend states based on historical price-SuperTrend proximity.

Formula

\text{VWMA}_{src} = \frac{\sum (Price \times Volume)}{\sum Volume} \\ \text{Upper} = \text{VWMA}_{src} + (Multiplier \times ATR) \\ \text{Lower} = \text{VWMA}_{src} - (Multiplier \times ATR) \\ \text{KNN Label} = \frac{\sum_{i=1}^{k} w_i \cdot L_i}{\sum w_i} \text{ where } w_i = \frac{1}{\text{dist}(x, x_i) + \epsilon}

Inputs

• k (Neighbors) (default: 3)
• n (Data points) (default: 10)
• KNN Price Length (default: 20)
• KNN ST Length (default: 100)
• maSrc (default: WMA)
• factor (default: 3.0)

See signal primitives and every published strategy that uses Volume SuperTrend AI (Expo) on WOBR StrategyVerse.