Maximizing Returns and Minimizing Risk: A Data-Driven Portfolio Optimization Analysis Using Markowitz's Theory and Sharpe Ratio

Abstract

The implementation of new technologies in the financial sphere is expanding as the era of big data arrives. This study uses Python tools to choose four representative stocks from 11 distinct industries for portfolio analysis, based on Markowitz's portfolio theory. The best portfolio solution is discovered through empirical research: Maximum Sharpe ratio of one; The anticipated return, standard deviation, and Sharpe ratio were compared and examined, and the effective frontier of the asset portfolio was calculated using Monte Carlo simulation. In addition, as a simple trading method, build two different simple moving averages. Backtesting reveals that the ideal portfolio's return rate is close to 20% using this strategy.