| Autor: |
Ayman Chaouki, Stephen Hardiman, Christian Schmidt, Emmanuel Sérié, Joachim de Lataillade |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Journal of Finance and Data Science, Vol 6, Iss , Pp 16-30 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9188 |
| DOI: |
10.1016/j.jfds.2020.06.002 |
| Popis: |
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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