Portfolio Optimization of Stocks – Python-Based Stock Analysis

Abstract

With the development of big data, blockchain artificial intelligence, and other technologies, the development of the financial industry also plays a great role in promoting the development of digital finance is also developing rapidly, the huge amount of financial data, the laws behind, randomness, the complexity have increased the difficulty of processing our data. The financial industry is also increasingly in need of data processing talents. For financial data such as: intra-day high-frequency data and stock price and volume data processing, Python has the points of fast calculation speed, open source, and excellent data visualization. In this paper, financial data analysis work based on the Python platform, six FTSE A50 constituent stocks of different industries are selected from the Chinese stock market, namely China Merchants Bank, SAIC Group, Haitong Securities, Capital Mining, China Unicom and Poly Development for financial data analysis. The optimal portfolio with the largest Sharpe ratio and the optimal portfolio with the smallest variance is obtained empirically by Python, and optimized by Monte Carlo simulation, and their expected returns, standard deviations, and Sharpe ratios are compared and analyzed, and finally, the effective boundaries of the asset portfolios are given. The importance of Markowitz‘s portfolio theory in financial risk management is further illustrated through empirical analysis.