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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Reinforcement Learning for Optimal Trading Rule Selection |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 56 |
| Date | 2022 |
| Abstract Text | With the rise of machine learning and artificial intelligence, new methods and resources for improving investment performance emerged and quickly gained popularity, owing to various benefits over human traders. In this thesis, two reinforcement learning agents are introduced to trade the S&P 500 E-Mini and the 10-Year US Treasury Notes futures contracts. The agents are trained and tested on 11 years of intraday data with added technical indicators. The agents’ performance is compared to a long-only benchmark. Our findings show that our agents did not outperform the benchmark and that they were unable to complete one testing session without overspending. |
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