Quantile Estimator
- Indicateurs
- Fillipe Dos Santos
- Version: 2.0
- Mise à jour: 6 janvier 2025
- Activations: 5
Quantile Estimator
Overview
This indicator implements a robust statistical method for analyzing price distributions and detecting outliers in financial markets. It's particularly valuable because it:
- Uses volume-weighted calculations for more accurate market representation
- Handles non-normal price distributions effectively
- Provides robust outlier detection through adaptive fencing
Key Components
Core Statistical Methods
- Harrell-Davis Quantile Estimator: Calculates weighted percentiles using beta distribution
- Quantile Absolute Deviation (QAD): Measures spread around the quantile estimate
- Double QAD Fencing: Creates adaptive upper/lower bounds for outlier detection
Main Parameters
- Sample Source: Default is HL2 (high-low average)
- Sample Size: Default 39 bars
- Probability: 0-100% (50% is median)
- Trim Percentage: Cuts low-density areas from calculations
- K-Multiplier: Controls fence width for outlier detection
Visual Output
- Blue line: Main quantile estimate
- Upper/lower bands: Outlier detection fences
- Shaded areas: Deviation regions
Comparison To Traditional Indicators
- Distribution Agnostic
- Works with any price distribution shape
- No normal distribution assumption required
- Robust to Outliers
- Trimming reduces impact of extreme values
- Adaptive fencing adjusts to market conditions
- Volume Integration
- Optional volume weighting for better market depth representation
- Can operate without volume data if needed
Usage Guidelines
For Trend Analysis
- Monitor main quantile line direction
- Watch for sustained breaks of fences
- Use larger sample sizes for longer-term trends
For Volatility Assessment
- Track fence width changes
- Monitor frequency of fence breaks
- Compare upper vs lower deviation areas
Implementation Notes
- Performance Considerations
- Volume weighting adds computation time
- Larger sample sizes increase calculation load
- Double QAD mode requires more processing
- Parameter Optimization
- K-multiplier affects signal sensitivity
- Trim percentage impacts calculation speed
- Sample size balances responsiveness vs stability
- Error Handling
- Handles missing volume data gracefully
- Maintains numerical stability with extreme values
- Provides fallbacks for edge cases