Portfolio Management Through Algorithmic Trading

Abstract

This chapter stresses how algorithmic trading has transformed portfolio management, underscoring the resultant ability to optimize risk-adjusted returns, enhance decision processes, and sustain efficient asset allocation. Advanced computational methods utilized in this research examine algorithmic strategies as a means to address the complexities of today’s financial markets in their ability to handle risk management, diversification, and periodic rebalancing. The results of the optimization of a portfolio consisting of six Nasdaq 100 stocks—Amazon, Apple, AMD, Tesla, Google, and NVIDIA—for ten years, from 2014 to 2024, are shown here. These assets have been selected based on their historical performance and variable risk-return profile as a sample to evaluate algorithmic trading strategies. In this paper, SLSQP is used to optimize the weights of each portfolio according to the Sharpe ratio, with efficient capital allocation considering the realistic constraint of no short-selling on the historical price data. Annual rebalancing was adopted to dampen the drifting of weights and to make the weights given at any period closer to the target weights. The performance of the portfolio is measured concerning the Nasdaq 100 through a set of key metrics: the cumulative return, the annualized return, volatility, and the Sharpe ratio. Hereby, the optimized portfolio gains an annualized return of 46.89% with a cumulative return of 4576.56% throughout the period under review. Although the portfolio demonstrated higher volatility (40.89%) in comparison to the Nasdaq 100, its Sharpe ratio of 1.12 surpassed that of the benchmark (0.90), thereby illustrating superior risk-adjusted performance. The rebalancing process effectively maintained the efficiency of the portfolio, although the concentration of risk in high-growth assets, such as NVIDIA, was brought to light. The findings highlight the inherent trade-offs between return maximization and risk management, offering valuable insights for investors, practitioners, and policymakers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.