opes 0.4.0

Open-source Python module for portfolio management with a plethora of portfolio schemes, stochastic backtesting and comprehensive metrics

OPES

An open-source Python library for advanced portfolio optimization and backtesting.

Overview

OPES provides a plethora of quantitative portfolio optimizers with a comprehensive backtesting engine. Test strategies against historical data with configurable slippage costs.

Key Features

Porfolio Objectives

Utility Theory

• Quadratic Utility
• Constant Relative Risk Aversion
• Constant Absolute Risk Aversion
• Hyperbolic Absolute Risk Aversion
• Kelly Criterion and Fractions

Markowitz Paradigm

• Maximum Mean
• Minimum Variance
• Mean Variance
• Maximum Sharpe

Principled Heuristics

• 1/N
• Risk Parity
• Inverse Volatility
• Softmax Mean
• Maximum Diversification
• Return Entropy Portfolio Optimization
• Discrete Entropy Portfolio Optimization

Risk Measures

• CVaR
• Mean-CVaR
• EVaR
• Mean-EvaR
• Entropic Risk Metric

Online Learning

• BCRP with regularization (FTL/FTRL support)
• Exponential Gradient

Distributionally Robust Optimization (Kullback-Leibler)

• Distributionally Robust Maximum Mean
• Distributionally Robust Kelly and Fractions

Slippage Models

• Constant
• Gamma
• Lognormal
• Inverse Gaussian
• Compound Poisson-Lognormal

Regularization Schemes

• L1
• L2
• Entropy
• Maximum Weight
• Weight Variance
• Gini Coefficient

Backtest Metrics

• Sharpe Ratio
• Sortino Ratio
• Volatility
• Average Return
• Total Return
• CAGR
• Maximum Drawdown
• Calmar Ratio
• Value at Risk 95
• Conditional Value at Risk 95
• Skew
• Kurtosis
• Omega Ratio

Portfolio Metrics

• Tickers
• Weights
• Portfolio Entropy
• Herfindahl Index
• Gini Coefficient
• Absolute Maximum Weight

Installation

pip install opes

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.