Open-source Python module for portfolio management with a plethora of portfolio schemes, stochastic backtesting and comprehensive metrics
Project description
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
- L2
- Entropy
- Maximum Weight
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
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.
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