[2603.15848] Algorithmic Trading Strategy Development and Optimisation

Computer Science > Artificial Intelligence arXiv:2603.15848 (cs) [Submitted on 16 Mar 2026 (v1), last revised 21 Mar 2026 (this version, v2)] Title:Algorithmic Trading Strategy Development and Optimisation Authors:Owen Nyo Wei Yuan, Victor Tan Jia Xuan, Ong Jun Yao Fabian, Ryan Tan Jun Wei View a PDF of the paper titled Algorithmic Trading Strategy Development and Optimisation, by Owen Nyo Wei Yuan and 3 other authors View PDF HTML (experimental) Abstract:The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being taken. The results show that the enhanced strategy significantly outperforms the baseline model in terms of total return, Sharpe ratio, and drawdown amongst other factors. The findings helped demonstrate the relevance and effectiveness of combining technical indicators, sentiment analysis, and computational optimisation in algorithmic trading systems. Comments: Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.7; F.2.2; F.2.3 Cite as: arXiv:2603.15848 [cs.AI]   (or arXiv:2603.15848v2 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.15848 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Owen Nyo Mr [view email] [...