Seeking better Sharpe ratio via Bayesian optimization
Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signa...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2023
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| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/7472 https://ink.library.smu.edu.sg/context/lkcsb_research/article/8471/viewcontent/seeking_better_sharpe.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signals. Simultaneously optimizing all these parameters to seek a high Sharpe ratio is a daunting and time-consuming task, partly because of the unknown mechanism determining the Sharpe ratio. This article proposes using Bayesian optimization to systematically search for the optimal parameter configuration that leads to a high Sharpe ratio. The author shows that the proposed intelligent search strategy performs better than manual search, a common practice that proves to be inefficient. The author’s framework also can easily be extended to other parameter selection tasks in portfolio optimization and risk management. |
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