Quickstart

Cvxportfolio is designed for ease of use and extension. With it, you can quickly experiment with financial trading strategies and develop new ones.

For example, a classic Markowitz optimization strategy

\[\begin{split}\begin{array}{ll} \text{maximize} & w^T \mathbf{E}[r] - \frac{1}{2} w^T \mathbf{Var}[r] w \\ \text{subject to} & w \geq 0, w^T \mathbf{1} <= 1 \end{array}\end{split}\]

is specified as follows

import cvxportfolio as cvx

objective = cvx.ReturnsForecast() - 0.5 * cvx.FullCovariance()
constraints = [cvx.LongOnly(), cvx.LeverageLimit(1)]

strategy = cvx.SinglePeriodOptimization(objective, constraints)

Here we instantiated an optimization-based trading policy. Cvxportfolio defines two (the other is a simple extension of this). Optimization-based policies are defined by an objective function, which is maximized, and a list of constraints, that are imposed on the solution. The objective function is specified as a linear combination of simple terms, we provide many, and it’s easy to define new ones. We provide as well many constraints to choose from, and it’s even easier to define new ones.

Where are the assets’ names?

Cvxportfolio policies are symbolic, they only define the logic of a trading strategy. They work with any selection of assets. The two objective terms defined above, however, need to know the values of the expected returns and the covariance matrix. You can pass Pandas dataframes for those, and in that case your dataframes should contain the assets’ names you want to trade. Or, if you want, or you can rely on forecasters to compute those (iteratively, in back-testing) using past data. That is what happens in the code shown above, the default parameters of cvxportfolio.ReturnsForecast and cvxportfolio.FullCovariance are forecasters that compute historical means and historical covariances respectively, at each point in time if running in a back-test or once, if running live, by using the policy’s execute() method.

To be continued.