What makes a good moving average?Four Core Qualities of Superior Moving Average
Accuracy (preserving large-scale structure):
Moving average should maintain the important underlying structure of price movements (like major trends and cycles) while filtering out all smaller fluctuations; it should faithfully represent the true price trajectory over longer timeframes.
Timeliness (minimal lag):
Most moving averages lag behind price action - they indicate changes way after they've already happened. A good moving average minimizes this lag, responding quickly to genuine price movements without sacrificing other qualities, providing more actionable signals and earlier entries/exits.
Minimal overshoot:
Overshoot occurs when a highly reactive moving average extends beyond the actual price extremes, creating false impressions of price levels never reached. TEMA, DEMA and HMA are examples of overshooting moving averages; good moving average should avoid this distortion, particularly during price reversals, preventing false triggers when used with threshold-based systems.
Smoothness (reduced noise):
A quality moving average filters out random price fluctuations (noise) that don't represent meaningful market activity, especially in steady non-volatile periods. This creates a clean, smooth line that clearly shows the underlying price direction without the jagged, erratic movements that could trigger false signals.
The Dynamic Adaptive Moving Average
Picture shows a study of Dynamic Adaptive Moving Average that employs a complex approach to price smoothing that continuously adjusts its behavior based on real-time market conditions. At its core, this indicator uses the ratio between short-term True Range and longer-term ATR to measure relative volatility changes in the market. This volatility assessment drives the automatic adjustment of critical smoothing parameters through calibrated sigmoid functions, allowing the indicator to become more responsive during volatile periods and more stable during consolidation.
Smoothing is achieved with three-stage filtering process: the first stage applies preliminary smoothing using self-adjusted adaptive exponential moving average. The second stage implements a Kalman filter that provides further smoothing while maintaining responsiveness to price spikes. The final stage applies another adaptive filter that balances smoothness and lag reduction.
The study shows comparison between HMA indicator and a working model of Dynamic Adaptive Moving Average.
