Volume Weighted SuperTrend | QuantumResearch

24 Feb

QuantumResearch Volume Weighted Supertrend (VWST)

The Volume Adaptive Supertrend (VWST) is a dynamic trend-following tool that enhances traditional Supertrend calculations by incorporating volume-weighted market conditions.

This indicator adapts its trend signals based on volume-adjusted price action, allowing traders to refine their entries and exits with improved responsiveness. 🚀📊

1. Key Features

• Volume-Weighted Adaptation: Uses various moving averages (VWMA, EMA, SMA, etc.) to adjust ATR bands based on market activity.
  
• Customizable Trend Sensitivity: Adjusts ATR multipliers separately for bullish and bearish conditions.
  
• Adaptive Supertrend Calculation: Dynamically recalculates trend direction based on volume-enhanced price movements.
  
• Multi-Timeframe Compatibility: Can be applied across different assets and timeframes for versatile market analysis.
  
• Visual Clarity & Alerts: Color-coded trend signals, shaded areas, and real-time alerts for trend shifts.
  

2. How It Works

A. Volume-Weighted Price Calculation

• The indicator applies a user-selected moving average (EMA, VWMA, SMA, etc.) to price and volume data
  
• This ensures that trend calculations are more reactive to strong volume surges and less influenced by low-liquidity fluctuations.
  

B. Adaptive ATR-Based Trend Filtering

• ATR bands are dynamically adjusted based on volume-weighted price action.
  
• Separate ATR multipliers for bullish and bearish conditions allow for refined sensitivity control.
  
• The Supertrend line shifts dynamically to reflect these conditions.
  

C. Signal Generation

• Bullish Trend: The price closes above the adaptive Supertrend line. ✅
  
• Bearish Trend: The price closes below the adaptive Supertrend line. ❌
  
• Long Entry: Triggered when trend direction switches from bearish to bullish.
  
• Short Entry: Triggered when trend direction switches from bullish to bearish.
  

3. Visual Representation

A. Color-Coded Trend Signals

• Green Trend Line: Indicates a bullish trend.
  
• Red Trend Line: Indicates a bearish trend.
  
• Gray Trend Line: Neutral phase.
  

B. Dynamic Background Fill

• Shaded Green Areas: Confirmed uptrend zones.
  
• Shaded Red Areas: Confirmed downtrend zones.
  

4. Customization & Parameters

• ATR Length & Multipliers: Adjust trend sensitivity with separate multipliers for bullish and bearish phases.
  
• Moving Average Type: Select from VWMA, EMA, SMA, HMA, WMA, DEMA, TEMA for volume-weighted calculations.
  
• Volume Length: Modify how far back volume data is used to refine trend calculations.
  
• Color Themes: Choose from 8 distinct color modes for clear visual representation.
  

5. Backtest & Market Applications

Backtest Summary:
The Volume Adaptive Supertrend (VWST) has been tested across multiple assets, including:

• BTC/USD
  
• ETH/USD
  
• SOL/USD
  

📊 Key Observations:

• Responsive Trend Detection: The volume-weighted adaptation helps minimize lag in trend shifts.
  
• Versatile Across Market Conditions: Works well in both trending and consolidating phases with appropriate settings.
  
• Customizable Risk Control: ATR multipliers can be adjusted to fine-tune signal sensitivity.
  

⚠️ Disclaimer: This indicator is designed to complement existing analysis techniques. Market conditions vary, and no tool can guarantee future performance. Always use proper risk management when trading.

6. Final Thoughts

• The Volume Weighted Supertrend (VWST) enhances traditional Supertrend indicators by incorporating volume-adjusted trend detection.
  
• Its dynamic ATR-based trend filtering ensures greater responsiveness to real market conditions.
  
• Suitable for trend traders, breakout traders, and risk-conscious investors looking for volume-driven confirmations.
  
• Use it alongside other confluences to build a robust trading system.
  
• Important Reminder: No single indicator guarantees profitability. Always validate signals with additional market context. 📊
  

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Catatan Rilis

Added the Switch button for the timeframe.