opes 0.6.0

A research-focused portfolio optimization and backtesting engine.

OPES

An Open-source Portfolio Estimation System for advanced portfolio optimization and backtesting.

---

Overview

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 a detailed walkthrough on this module.

---

Disclaimer

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.

---

Portfolio Objectives

Classification	Name
Utility Theory	Quadratic Utility
	Constant Relative Risk Aversion (CRRA)
	Constant Absolute Risk Aversion (CARA)
	Hyperbolic Absolute Risk Aversion (HARA)
	Kelly Criterion & Fractional Kelly
Markowitz Paradigm	Maximum Mean Return
	Minimum Variance
	Mean-Variance
	Maximum Sharpe Ratio
Principled Heuristics	Uniform (1/N)
	Risk Parity
	Inverse Volatility
	Softmax Mean
	Maximum Diversification
	Return Entropy Portfolio Optimization
Risk Measures	Value at Risk (VaR)
	Conditional Value at Risk (CVaR)
	Mean-CVaR
	Entropic Value at Risk (EVaR)
	Mean-EVaR
	Entropic Risk Measure
	Worst-Case Loss
Online Learning	Best Constant Rebalanced Portfolio (BCRP)
	Exponential Gradient
Distributionally Robust Optimization	KL-Ambiguity Robust Maximum Mean Return
	KL-Ambiguity Robust Kelly
	KL-Ambiguity Robust Fractional Kelly
	Wasserstein-Ambiguity Robust Maximum Mean Return

Metrics

Portfolio Metrics	Backtest Metrics
Tickers	Sharpe
Weights	Sortino
Portfolio Entropy	Volatility
Herfindahl Index	Average Return
Gini Coefficient	Total Return
Absolute Maximum Weight	Maximum Drawdown
	Value-at-Risk 95
	Conditional-Value-at-Risk 95
	Skew
	Kurtosis
	Omega Ratio

Other Features

Slippage Models	Regularization Schemes
Constant	L1 Regularization
Gamma	L2 Regularization
Lognormal	L-infinity Regularization
Inverse Gaussian	Entropy
Compound Poisson-Lognormal	Weight Variance
	Mean Pairwise Absolute Deviation
	KL-Divergence from Uniform (Experimental)
	JS-Divergence from Uniform (Experimental)

---

Upcoming Features (Unconfirmed)

These features are still in the works and may or may not appear in later updates:

• Hierarchical Risk Parity (Principled Heuristics)

• Universal Portfolios (Online Learning)

• Online Newton Step (Online Learning)

• ADA-BARRONS (Online Learning)

• Wasserstein Ambiguity Duals (Distributionally Robust)

  • Global Minimum Variance (GMV)
  • Mean–Variance Optimization (MVO)