Application of Python in Portfolio Optimization

Abstract

Portfolio optimization is the approach to select the optimal portfolio that provides the most profitable rate of return for each unit of risk taken by an investor. An investment portfolio is the distribution of an investor's assets, alternatively, it is the selection pool of an investor's investments. The objective of this thesis is to validate and compare the out-of-sample performance of the following strategies: navie Strategy, minimum variance and maximum Sharpe ratio. Therefore, we chose thirty stocks that listed on the NASDAQ Composite Index during the past ten years. This thesis is divided into five chapters. The first chapter expounds on the content and structure of the thesis. The second chapter introduces Python. In Chapter 3 we describe the methodology for portfolio optimization. In the fourth chapter, we use Python to compute the naive Strategy, minimum variance and maximu Sharpe ratio portfolios. The last chapter is the conclusion.