Seeking better Sharpe ratio via Bayesian optimization

• Main Author: LIU, Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Finance; Finance and Financial Management
• 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
• Institution: Singapore Management University

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.