Risk Distribution Histogram

Statistical risk visualization and analysis tool for any ticker 📊
The Risk Distribution Histogram visualizes the statistical distribution of different risk metrics for any financial instrument. It converts risk data into histograms with quartile-based color coding, so that traders can understand their risk, tail-risks, exposure patterns and make data-driven decisions based on empirical evidence rather than assumptions.

The indicator supports multiple risk calculation methods, each designed for different aspects of market analysis, from general volatility assessment to tail risk analysis.

Risk Measurement Methods
Standard Deviation
Captures raw daily price volatility by measuring the dispersion of price movements. Ideal for understanding overall market conditions and timing volatility-based strategies.
Use case: Options trading and volatility analysis.

Average True Range (ATR)
Measures true range as a percentage of price, accounting for gaps and limit moves. Valuable for position sizing across different price levels.
Use case: Position sizing and stop-loss placement.

The chart above illustrates how ATR statistical distribution can be used by looking at the ATR % of price distribution. For example, 90% of the movements are below 5%.

Downside Deviation
Only considers negative price movements, making it ideal for checking downside risk and capital protection rather than capturing upside volatility.
Use case: Downside protection strategies and stop losses.

Drawdown Analysis
Tracks peak-to-trough declines, providing insight into maximum loss potential during different market conditions.
Use case: Risk management and capital preservation.

The chart above illustrates tale risk for the asset (TQQQ), showing that it is possible to have drawdowns higher than 20%.

Entropy-Based Risk (EVaR)
Uses information theory to quantify market uncertainty. Higher entropy values indicate more unpredictable price action, valuable for detecting regime changes.
Use case: Advanced risk modeling and tail-risk.

VIX Histogram
Incorporates the market's fear index directly into analysis, showing how current volatility expectations compare to historical patterns. The VIX histogram is independent from the ticker on the chart.
Use case: Volatility trading and market timing.

Visual Features
The histogram uses quartile-based color coding that immediately shows where current risk levels stand relative to historical patterns:

• Green (Q1): Low Risk (0-25th percentile)
  
• Yellow (Q2): Medium-Low Risk (25-50th percentile)
  
• Orange (Q3): Medium-High Risk (50-75th percentile)
  
• Red (Q4): High Risk (75-100th percentile)
  

The data table provides detailed statistics, including:

• Count Distribution: Historical observations in each bin
  
• PMF: Percentage probability for each risk level
  
• CDF: Cumulative probability up to each level
  
• Current Risk Marker: Shows your current position in the distribution
  

Trading Applications
When current risk falls into upper quartiles (Q3 or Q4), it signals conditions are riskier than 50-75% of historical observations. This guides position sizing and portfolio adjustments.
Key applications:

• Position sizing based on empirical risk distributions
  
• Monitoring risk regime changes over time
  
• Comparing risk patterns across timeframes
  

Risk distribution analysis improves trade timing by identifying when market conditions favor specific strategies.

• Enter positions during low-risk periods (Q1)
  
• Reduce exposure in high-risk periods (Q4)
  
• Use percentile rankings for dynamic stop-loss placement
  
• Time volatility strategies using distribution patterns
  
• Detect regime shifts through distribution changes
  
• Compare current conditions to historical benchmarks
  
• Identify outlier events in tail regions
  
• Validate quantitative models with empirical data
  

Configuration Options
Data Collection

• Lookback Period: Control amount of historical data analyzed
  
• Date Range Filtering: Focus on specific market periods
  
• Sample Size Validation: Automatic reliability warnings
  

Histogram Customization

• Bin Count: 10-50 bins for different detail levels
  
• Auto/Manual Bin Width: Optimize for your data range
  
• Visual Preferences: Custom colors and font sizes
  

Implementation Guide
Start with Standard Deviation on daily charts for the most intuitive introduction to distribution-based risk analysis.

• Method Selection: Begin with Standard Deviation
  
• Setup: Use daily charts with 20-30 bins
  
• Interpretation: Focus on quartile transitions as signals
  
• Monitoring: Track distribution changes for regime detection
  

The tool provides comprehensive statistics including mean, standard deviation, quartiles, and current position metrics like Z-score and percentile ranking.

Enjoy, and please let me know your feedback! 😊🥂