Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Monaco, Vito Alessandro Riva, Antonio Sabbioni, Luca Bisi, Lorenzo Vittori, Edoardo Pinciroli, Marco Trapletti, Michele Restelli, Marcello |
| author_facet | Monaco, Vito Alessandro Riva, Antonio Sabbioni, Luca Bisi, Lorenzo Vittori, Edoardo Pinciroli, Marco Trapletti, Michele Restelli, Marcello |
| contents | In recent years, the popularity of artificial intelligence has surged due to its widespread application in various fields. The financial sector has harnessed its advantages for multiple purposes, including the development of automated trading systems designed to interact autonomously with markets to pursue different aims. In this work, we focus on the possibility of recognizing and leveraging intraday price patterns in the Foreign Exchange market, known for its extensive liquidity and flexibility. Our approach involves the implementation of a Reinforcement Learning algorithm called Fitted Natural Actor-Critic. This algorithm allows the training of an agent capable of effectively trading by means of continuous actions, which enable the possibility of executing orders with variable trading sizes. This feature is instrumental to realistically model transaction costs, as they typically depend on the order size. Furthermore, it facilitates the integration of risk-averse approaches to induce the agent to adopt more conservative behavior. The proposed approaches have been empirically validated on EUR-USD historical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic Monaco, Vito Alessandro Riva, Antonio Sabbioni, Luca Bisi, Lorenzo Vittori, Edoardo Pinciroli, Marco Trapletti, Michele Restelli, Marcello Trading and Market Microstructure Machine Learning Computational Finance In recent years, the popularity of artificial intelligence has surged due to its widespread application in various fields. The financial sector has harnessed its advantages for multiple purposes, including the development of automated trading systems designed to interact autonomously with markets to pursue different aims. In this work, we focus on the possibility of recognizing and leveraging intraday price patterns in the Foreign Exchange market, known for its extensive liquidity and flexibility. Our approach involves the implementation of a Reinforcement Learning algorithm called Fitted Natural Actor-Critic. This algorithm allows the training of an agent capable of effectively trading by means of continuous actions, which enable the possibility of executing orders with variable trading sizes. This feature is instrumental to realistically model transaction costs, as they typically depend on the order size. Furthermore, it facilitates the integration of risk-averse approaches to induce the agent to adopt more conservative behavior. The proposed approaches have been empirically validated on EUR-USD historical data. |
| title | Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic |
| topic | Trading and Market Microstructure Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2410.23294 |