VaultCharts
distribution Metric

What Is Skewness?

Skewness measures the asymmetry of the return distribution—whether large gains or large losses are more frequent.

Quick Answer

Skewness measures the asymmetry of the return distribution—whether large gains or large losses are more frequent.

What Does Skewness Measure?

Skewness is the third moment of the return distribution. Positive skew means the right tail (large gains) is longer; negative skew means the left tail (large losses) is more pronounced. Many traders prefer positive skew: small frequent losses and occasional large gains. Negative skew can mean “picking up nickels in front of a steamroller”—small gains with rare but severe losses.

Formula:
Skewness = E[(R - μ)^3] / σ^3 (μ = mean, σ = std dev)

Typical range: Roughly -2 to +2 for many strategies; can be more extreme

How to Interpret Skewness

  • 1Skewness > 0: right tail longer; more large positive returns
  • 2Skewness < 0: left tail longer; more large negative returns
  • 3Zero skew: symmetric distribution (e.g. normal)
  • 4Option selling and mean reversion often show negative skew

How to Use Skewness in Backtesting & Portfolio Analysis

Understand if strategy has “lottery” (positive) or “steamroller” (negative) profile
Complement Sharpe and Sortino with distribution shape
Compare strategies that have similar returns but different skew
Assess suitability for risk tolerance and drawdown tolerance

Common Mistakes to Avoid

Ignoring negative skew when strategy has rare blow-ups
Assuming average return tells the full story when skew is extreme
Using skewness alone without kurtosis and drawdowns
Expecting skew to be stable across different market regimes

Backtest with Skewness in VaultCharts

VaultCharts includes backtesting with built-in and custom strategies. Analyze Skewness, Sharpe ratio, max drawdown, and more—all with your data stored locally.

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