Gaussian Filter
period = 14 (2–200) • sigma = 1 (0.1–5) Overview
The Gaussian Filter is a sophisticated smoothing technique that applies a Gaussian (normal) distribution to weight price data. Unlike simple moving averages that give equal weight to all data points, the Gaussian Filter assigns the highest weight to the most recent data and progressively lower weights to older data following a bell curve pattern. This results in a smoother output that responds more quickly to recent price changes while filtering out market noise.
Introduced by John F. Ehlers, the filter models price data using the Gaussian distribution and uses standard deviation (sigma) to select high-probability data points while filtering out outliers that constitute market noise. This makes it particularly effective for identifying the main market trend without being easily influenced by sudden and temporary price changes.
Interpretation & Trading Signals
Trend Identification:
- Uptrend: Price consistently above the Gaussian Filter line with filter sloping upward
- Downtrend: Price consistently below the Gaussian Filter line with filter sloping downward
- Trend Strength: Steeper slope indicates stronger trend momentum
- Dynamic Support/Resistance: Filter often acts as support in uptrends and resistance in downtrends
Trading Signals:
- Buy Signal: Price crosses above the Gaussian Filter from below (trend continuation or reversal)
- Sell Signal: Price crosses below the Gaussian Filter from above
- Confirmation: Wait for filter direction to align with the crossover signal
- Exit Signals: Close positions when price moves back through the filter
Parameter Guidelines:
- Period (10-50): Higher values = more smoothing, fewer false signals, increased lag
- Sigma (0.5-3.0): Higher values = wider distribution, considers more data points
- For Scalping: Use lower period (10-20) with sigma 0.5-1.0 for responsiveness
- For Swing Trading: Use higher period (30-50) with sigma 1.5-2.5 for stability
Example Usage
Code examples will be available once the Rust implementation is complete.
Performance Analysis
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