  Portfolio diversification is one of the most effective ways to minimize
investment risk. Individuals and fund managers aim to create a portfolio of
assets that not only have high returns but are also uncorrelated. This goal can
be achieved by comparing the historical performance, fundamentals, predictions,
news sentiment, and many other parameters that can affect the portfolio's
value. One of the most well-known approaches to manage/optimize portfolios is
the well-known mean-variance (Markowitz) portfolio. The algorithm's inputs are
the expected returns and risks (volatility), and its output is the optimized
weights for each asset in the target portfolio. Simplified unrealistic
assumptions and constraints were used in its original version preventing its
use in practical cases. One solution to improve its usability is by altering
the parameters and constraints to match investment goals and requirements. This
paper introduces PortFawn, an open-source Python library to create and backtest
mean-variance portfolios. PortFawn provides simple-to-use APIs to create and
evaluate mean-variance optimization algorithms using classical computing
(real-valued asset weights) as well as quantum annealing computing (binary
asset weights). This tool has many parameters to customize the target
portfolios according to the investment goals. The paper introduces the
background and limitations of the mean-variance portfolio optimization
algorithm, its architecture, and a description of the functionalities of
PortFawn. We also show how one can use this tool in practice using a simple
investment scenario.
