Portfolio Optimization is used for risk-averse investors to construct portfolios to optimize or maximize expected return based on a given level of market risk, emphasizing that risk is an inherent part of higher reward
- Runs an example Monte Carlo Simulation for an optimal portfolio with resulting returns
- Creates an Efficient Frontier which is used to identify a set of optimal portfolios that offers the highest expected return for a defined level of risk or the lowest risk for a given level of expected return
Monte Carlo Simulation for Optimization Search¶
Monte Carlo simulations are used by analyst to determine the expected value and optimal distribution of a portfolio.
import numpy as np import pandas as pd import hvplot.pandas # noqa