cvxportfolio 0.2.0

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Cvxportfolio

WORK IN PROGRESS. Cvxportfolio is currently under development. We will freeze the user interface by end of 2023Q2 and release the first stable version by end of 2023Q3. The script hello_world.py now runs with the new interface (see below).

cvxportfolio is a python library for portfolio optimization and simulation based on the book Multi-Period Trading via Convex Optimization. It is written in Python, its main dependencies are cvxpy and pandas.

The documentation of the package is at cvxportfolio.readthedocs.io.

Installation

pip install cvxportfolio

Testing

To test it locally, for example, you can set up the development environment with poetry and run pytest.

git clone https://github.com/cvxgrp/cvxportfolio.git
cd cvxportfolio
poetry install
poetry run pytest --cov

Example

To get a sneak preview of cvxportfolio you may try the following code. This is available in examples/hello_world.py and runs with cvxportfolio >= 0.2.0

import cvxportfolio as cp
import matplotlib.pyplot as plt

# define a portfolio optimization policy
# with rolling window mean (~10 yrs) returns
# with forecast error risk on returns (see the book)
# rolling window mean (~10 yrs) covariance
# and forecast error risk on covariance (see the book)
policy = cp.SinglePeriodOptimization(objective = 
        cp.RollingWindowReturnsForecast(2500) -
        cp.RollingWindowReturnsForecastErrorRisk(2500) -
        5 * cp.RollingWindowFullCovariance(2500, forecast_error_kappa = 0.25), 
        constraints = [cp.LeverageLimit(3)]
        )
        
# define a market simulator, which downloads stock market data and stores it locally
# in ~/cvxportfolio/        
simulator = cp.MarketSimulator(["AMZN", "AAPL", "MSFT", "GOOGL", "TSLA", "GM"])

# perform a backtest (by default it starts with 1E6 USD cash)
backtest = cp.BackTest(policy, simulator, '2023-01-01', '2023-04-21')

# plot value of the portfolio in time
backtest.v.plot(figsize=(12, 5), label='Single Period Optimization')
plt.ylabel('USD')
plt.title('Total value of the portfolio in time')
plt.show()

# plot weights of the (non-cash) assets for the SPO policy
backtest.w.iloc[:, :-1].plot()
plt.title('Weights of the portfolio in time')
plt.show()

print('total tcost', backtest.tcost.sum())
print('total borrow cost', backtest.hcost_stocks.sum())
print('total cash return + cost', backtest.hcost_cash.sum())

(The other examples may currently have problems as we are changing various bits and pieces of cvxportfolio.)

Academic

If you use cvxportfolio in your academic work please cite our book:

@article{BBDKKNS:17,
    author       = {S. Boyd and E. Busseti and S. Diamond and R. Kahn and K. Koh and P. Nystrup and J. Speth},
    title        = {Multi-Period Trading via Convex Optimization},
    journal      = {Foundations and Trends in Optimization},
    year         = {2017},
    month        = {August},
    volume       = {3},
    number       = {1},
    pages        = {1--76},
    publisher    = {Now Publishers},
    url          = {http://stanford.edu/~boyd/papers/cvx_portfolio.html},
}

License

Cvxportfolio is licensed under the Apache 2.0 permissive open source license.