Algorithmic Trading Strategy Development and Optimisation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914412065980416 |
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| author | Yuan, Owen Nyo Wei Xuan, Victor Tan Jia Fabian, Ong Jun Yao Wei, Ryan Tan Jun |
| author_facet | Yuan, Owen Nyo Wei Xuan, Victor Tan Jia Fabian, Ong Jun Yao Wei, Ryan Tan Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15848 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Algorithmic Trading Strategy Development and Optimisation Yuan, Owen Nyo Wei Xuan, Victor Tan Jia Fabian, Ong Jun Yao Wei, Ryan Tan Jun Artificial Intelligence I.2.7; F.2.2; F.2.3 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. |
| title | Algorithmic Trading Strategy Development and Optimisation |
| topic | Artificial Intelligence I.2.7; F.2.2; F.2.3 |
| url | https://arxiv.org/abs/2603.15848 |