Metadata-Version: 2.4
Name: opes
Version: 0.1.1
Summary: Open-source Python module for portfolio management with a plethora of portfolio schemes, stochastic backtesting and comprehensive metrics
Author-email: Nitin Tony Paul <nitintonypaul@gmail.com>
License: MIT
Project-URL: repository, https://github.com/opes-core/opes
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Office/Business :: Financial :: Investment
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<3.0,>=2.2.6
Requires-Dist: pandas<3.0,>=2.3.3
Requires-Dist: scipy<2.0,>=1.15.2
Dynamic: license-file

# 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

```python
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.
