Variable Length Moving Average (VLMA)
period = 14 (5–50) • volatility_period = 9 (5–30) Overview
The Variable Length Moving Average represents an evolution in moving average technology by incorporating adaptive behavior based on market volatility. First introduced in Technical Analysis of Stocks & Commodities magazine in March 1992, the VLMA uses a volatility index (often the Chande Momentum Oscillator or standard deviation) to adjust its effective length dynamically. When volatility increases, the average speeds up to capture trend changes quickly; when volatility decreases, it slows down to filter out market noise.
The calculation follows the formula VMA = α × VI × Close + (1 - α × VI) × Previous VMA, where VI is the volatility index and α is the smoothing constant (2/(N+1)). This approach allows the indicator to automatically adjust its sensitivity without manual intervention, making it particularly effective in markets that alternate between trending and ranging phases. The result is a moving average that remains responsive to genuine price movements while avoiding excessive whipsaws during consolidation periods.
Interpretation & Trading Signals
Price Crossover Signals:
- Bullish Signal: Price crosses above VLMA - potential long entry
- Bearish Signal: Price crosses below VLMA - potential short entry
- Adaptive Response: Faster signals in volatile markets, fewer whipsaws in quiet markets
- Confirmation: Strong trends show clear separation from VLMA
Trend Analysis:
- Dynamic Support: VLMA acts as support in uptrends when volatility is low
- Dynamic Resistance: VLMA provides resistance in downtrends
- Trend Strength: Steeper VLMA slope indicates stronger trend momentum
- Volatility Adaptation: VLMA automatically adjusts to market conditions
Multiple VLMA Strategy:
- Dual VLMA: Use fast and slow VLMA for crossover signals
- VLMA + Traditional MA: Combine with SMA/EMA for confirmation
- Volatility Filters: Trade only when volatility index confirms trend
- Risk Management: Use VLMA as trailing stop in trending markets
Example Usage
Code examples will be available once the Rust implementation is complete.
Performance Analysis
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