Algorithmic Trading Strategy Development and Optimisation

Computer Science > Artificial Intelligence [Submitted on 16 Mar 2026] Algorithmic Trading Strategy Development and Optimisation Owen Nyo Wei Yuan, Victor Tan Jia Xuan, Ong Jun Yao Fabian, Ryan Tan Jun Wei 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: 27 pages, 7 figures 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:2603.15848v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.15848 Focus to learn more Submission history From: Owen Nyo Mr [view email] [v1] Mon, 16 Mar 2026 19:29:37 UTC (551 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)