Deep Learning for Options Trading: An End-To-End Approach
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913582268022784 |
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| author | Tan, Wee Ling Roberts, Stephen Zohren, Stefan |
| author_facet | Tan, Wee Ling Roberts, Stephen Zohren, Stefan |
| contents | We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Options Trading: An End-To-End Approach Tan, Wee Ling Roberts, Stephen Zohren, Stefan Portfolio Management Machine Learning Computational Finance Trading and Market Microstructure We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs. |
| title | Deep Learning for Options Trading: An End-To-End Approach |
| topic | Portfolio Management Machine Learning Computational Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2407.21791 |