In the CAPM and APT pricing theories, we assumed linearity in the models and solved for expected security prices using regressions in Python.
As the number of securities in our portfolio increases, certain limitations are introduced as well. Portfolio managers would find themselves constrained by these rules in pursuing certain objectives mandated by investors.
Linear optimization helps overcome the problem of portfolio allocation. Optimization focuses on minimizing or maximizing the value of objective functions. Some examples include maximizing returns and minimizing volatility. These objectives are usually governed by certain regulations, such as a no short-selling rule, or limits on the number of securities to be invested.
Unfortunately, in Python, there is no single official package that supports this solution. However, there are third-party packages available...
