opes 0.1.1

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

• 15+ portfolio schemes: Mean-Variance, Kelly Criterion, CVaR, Exponential Gradient and more
• Advanced backtesting: Historical performance analysis with comprehensive metrics
• Stochastic slippage models: Gamma, Lognormal, Inverse Gaussian, Poisson Jump or constant costs
• Flexible regularization: Entropy, L2, and MaxWeight regularizers
• Rich metrics: Sharpe, Sortino, Calmar, Max Drawdown, Skewness, Kurtosis and more

Portfolio Methods

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

• Risk Parity
• Inverse Volatility
• Softmax Mean
• Maximum Diversification
• 1/N

Risk Measures

• CVaR
• Mean-CVaR
• EVaR
• Mean-EvaR

Online Learning

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

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.