Open-source Python module for portfolio management with a plethora of portfolio schemes, stochastic backtesting and 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 (stochastic or constant).
Key Features
- 15+ optimizers (and more to come): Mean-Variance, Max Sharpe, Kelly Criterion, Risk Parity, CVaR, Online Learning models and more
- Advanced backtesting: Historical performance analysis with wealth plots and comprehensive metrics
- Stochastic slippage models: Gamma, Lognormal, Poisson Jump, Inverse Gaussian, or constant costs
- Flexible regularization: Entropy, L2, and MaxWeight regularizers
- Rich metrics: Sharpe, Sortino, Calmar, Max Drawdown, CVaR, VaR, CAGR, Skewness, Kurtosis and more
Portfolio Methods
Utility Theory
- Quadratic Utility
- CRRA
- CARA
- HARA
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
Release history Release notifications | RSS feed
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