An Open-source Portfolio Estimation System for advanced portfolio optimization and backtesting.
OPES is a comprehensive Python library for advanced portfolio optimization and backtesting. Designed for quantitative finance enthusiasts, OPES provides a wide range of portfolio strategies, risk measures and robust evaluation tools.
Visit the documentation for quick insights.
For a detailed guide on this module, visit the guide.
The information provided by OPES is for educational, research and informational purposes only. It is not intended as financial, investment or legal advice. Users should conduct their own due diligence and consult with licensed financial professionals before making any investment decisions. OPES and its contributors are not liable for any financial losses or decisions made based on this content. Past performance is not indicative of future results.
- Quadratic Utility
- Constant Relative Risk Aversion
- Constant Absolute Risk Aversion
- Hyperbolic Absolute Risk Aversion
- Kelly Criterion and Fractions
- Maximum Mean
- Minimum Variance
- Mean Variance
- Maximum Sharpe
- Uniform (1/N)
- Risk Parity
- Inverse Volatility
- Softmax Mean
- Maximum Diversification
- Return Entropy Portfolio Optimization
- Conditional Value at Risk
- Mean-CVaR
- Entropic Value at Risk
- Mean-EVaR
- Entropic Risk Measure
- BCRP with weight regularization (FTL/FTRL support)
- Exponential Gradient
- KL-Ambiguity Distributionally Robust Maximum Mean
- KL-Ambiguity Distributionally Robust Kelly and Fractions
- Wasserstein-Ambiguity Distributionally Robust Maximum Mean
- Constant
- Gamma
- Lognormal
- Inverse Gaussian
- Compound Poisson-Lognormal
- L1
- L2
- L-infinity
- Entropy
- Weight Variance
- Mean Pairwise Absolute Deviation
- Sharpe Ratio
- Sortino Ratio
- Volatility
- Average Return
- Total Return
- Maximum Drawdown
- Value at Risk 95
- Conditional Value at Risk 95
- Skew
- Kurtosis
- Omega Ratio
- Tickers
- Weights
- Portfolio Entropy
- Herfindahl Index
- Gini Coefficient
- Absolute Maximum Weight
These features are still in the works and may or may not appear in later updates:
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Mean–Variance–Skew–Kurtosis Optimization (Markowitz)
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Hierarchical Risk Parity (Principled Heuristics)
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Online Newton Step (Online Learning)
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Ada Barrons (Online Learning)
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Wasserstein Ambiguity Duals (Distributionally Robust)
- Global Minimum Variance (GMV)
- Mean–Variance Optimization (MVO)
- Kelly Criterion