Installation

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This page guides you through installing OPES for experimentation & research.

Warning:

OPES is currently under development. While it is relatively stable for experimentation, some features may change or break. Use at your own discretion and always verify results when testing.

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Procedure

Python 3.10+ is required for opes to run (although it may work on some lower versions). opes is tested upto Python 3.14. To install a stable release of opes, pip is recommended for convenience.

Installation

You can install OPES easily via PyPI using pip.

pip install opes

This will fetch the latest stable release and all required dependencies. Alternatively, you are also welcome to install the module directly from GitHub.

git clone https://github.com/opes-core/opes.git
cd opes-main
pip install .

You can also install in editable mode if you plan on making any changes to the source code.

# After cloning and in the root of the project
pip install -e .

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Verification

After installation, make sure everything works by opening Python and importing opes.

>>> import opes
>>> opes.__version__
>>> '0.10.0' # May not be the current version but you get the idea

You can also verify your installation by using pip.

pip show opes

If no errors appear, opes is ready to use.

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Getting Started

opes is designed to be minimalistic and easy to use and learn for any user. Here is an example script which implements my favorite portfolio, the Kelly Criterion.

# I recommend you use yfinance for testing.
# However for serious research, using an external, faster API would be more fruitful.
import yfinance as yf

# Importing our Kelly class
from opes.objectives import Kelly

# ---------- FETCHING DATA ----------
# Obtaining ticker data
# Basic yfinance stuff
TICKERS = ["AAPL", "NVDA", "PFE", "TSLA", "BRK-B", "SHV", "TLT"]
asset_data = yf.download(
    tickers=TICKERS, 
    start="2010-01-01", 
    end="2020-01-01", 
    group_by="ticker", 
    auto_adjust=True
)

# ---------- OPES USAGE ----------
# Initialize a Kelly portfolio with fractional exposure and L2 regularization
# Fractional exposure produces less risky weights and L2 regularization contributes in penalizing concentration
kelly_portfolio = Kelly(fraction=0.8, reg="l2", strength=0.001)

# Compute portfolio weights with custom weight bounds
kelly_portfolio.optimize(asset_data, weight_bounds=(0.05, 0.8))

# Clean negligible allocations
cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)

# Output the final portfolio weights
print(cleaned_weights)

This showcases the simplicity of the module. However there are far more diverse features you can still explore. If you're looking for more examples, preferably some of them with "context", I recommend you check out the examples page within the documentation.