Difference between revisions of "Applications of Reinforcement Learning in Finance"
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* [https://drive.google.com/drive/folders/1bksxRmJpy037pzALmeHiKWD7e8eBWBbh?usp=sharing Google Drive with paper and links] | * [https://drive.google.com/drive/folders/1bksxRmJpy037pzALmeHiKWD7e8eBWBbh?usp=sharing Google Drive with paper and links] | ||
* [https://arxiv.org/abs/2206.14267 Arxiv] | * [https://arxiv.org/abs/2206.14267 Arxiv] | ||
| + | * [https://www.overleaf.com/read/sbydjmfrkksf Presentation Overleaf] | ||
== Data == | == Data == | ||
Revision as of 07:21, 7 July 2022
Details
- Authors: Frensi Zejnullahu, Maurice Moser
- Title: Applications of Reinforcement Learning in Finance - Trading with a Double Deep Q-Network
- Supervisior: Prof. Dr. Jörg Osterrieder
- Degree: Bachelor of Science
- University: Zurich University of Applied Sciences
- Year: 2022
- Status: Working Paper
Summary
Analyzing the performance and behavior of an RL agent with respect to additional features and environmental conditions. These include increasing features with other assets, trading costs, and pre- and post-crisis testing.
Abstract
This paper presents a Double Deep Q-Network algorithm for trading single assets, namely the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for our environment with multiple extensions. The features of our trading agent are constantly being expanded to include additional assets such as commodities, resulting in four models. We also respond to environmental conditions, including costs and crises. Our trading agent is first trained for a specific time period and tested on new data and compared with the long-and-hold strategy as a benchmark (market). We analyze the differences between the various models and the in-sample/out-of-sample performance with respect to the environment. The experimental results show that the trading agent follows an appropriate behavior. It can adjust its policy to different circumstances, such as more extensive use of the neutral position when trading costs are present. Furthermore, the net asset value exceeded that of the benchmark, and the agent outperformed the market in the test set. We provide initial insights into the behavior of an agent in a financial domain using a DDQN algorithm. The results of this study can be used for further development.
Important links
Data
- S&P500 Future (ESc1)
- Russell 2000 ETF (IWM)
- Gold Future (GCc1)
- WTI Crude Oil Future (CLc1)
Data source: Refinitiv