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OmegaRatio Node

Probability-weighted gains vs losses metric

StatisticalRisk-ReturnProbability

Overview

Omega Ratio measures expected gains above a threshold divided by expected losses below that threshold, both probability-weighted. It's superior to Sharpe Ratio because it accounts for actual return distribution shape (skewness, kurtosis) rather than assuming normality. Omega > 2.0 is excellent; Omega > 1.0 is good.

Unlike Sharpe which penalizes all volatility equally, Omega only penalizes downside volatility. This better captures what traders care about: making more on winners than losing on losers. A strategy with Omega = 3.0 wins 3x more on gains than it loses on declines above/below threshold.

Formula & Calculation

Omega Ratio
Ω = ∫₀^∞ [1 - F(r)] dr / ∫₋∞^0 F(r) dr
F(r) = Distribution of returns
Threshold: Usually 0% (risk-free rate or MAR)
Gains area above threshold / Losses area below threshold
Practical Form
Ω = E[max(R - threshold, 0)] / E[max(threshold - R, 0)]
Numerator: Expected upside beyond threshold
Denominator: Expected downside below threshold

Parameters

ParameterDefaultDescription
period252Trading days for calculation
threshold0.0%Minimum acceptable return (MAR)

Common Use Cases

1. Strategy Comparison

Compare strategies on Omega basis: Strategy A (Omega=2.5) beats Strategy B (Omega=1.2) despite similar Sharpe. Omega captures real preferences.

2. Portfolio Optimization

Maximize portfolio Omega instead of Sharpe. Weights toward strategies with high upside probability and low downside exposure.

3. Risk Threshold Setting

Omega shows margin-of-safety: If threshold = 0%, Omega = 2 means 2x more gains than losses. Set stops and targets to match threshold.

4. Distribution Analysis

Omega > Sharpe suggests positive skew (good). Omega < Sharpe suggests negative skew (risky). Different thresholds reveal distribution shape.

Advantages & Limitations

Advantages

  • No normality assumption
  • Only penalizes downside
  • Threshold-flexible (set own MAR)
  • Captures distribution shape
!

Limitations

  • Threshold selection critical
  • Requires sufficient samples
  • Unstable for low-probability outcomes
  • Hard to benchmark (vs Sharpe)

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