Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security
Stock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an ada...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10872909/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857135130247168 |
|---|---|
| author | Haogang Feng Yue Wang Shida Zhong Tao Yuan Zhi Quan |
| author_facet | Haogang Feng Yue Wang Shida Zhong Tao Yuan Zhi Quan |
| author_sort | Haogang Feng |
| collection | DOAJ |
| description | Stock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an adaptive trade execution framework that leverages joint reinforcement learning. The FPPO algorithm demonstrates significant improvements in model performance across various stocks, with average returns enhanced by 3% to 15%. It also exhibits superior performance in key metrics such as the reward function value, showcasing its effectiveness in different financial contexts. The paper further explores the model’s performance under the FPPO algorithm with varying numbers of client nodes and different risk preferences, underscoring the importance of these factors in model construction. The results substantiate the FPPO algorithm’s capability to safeguard privacy, ensure high performance, and enable the creation of personalized trading models in the optimal trade execution problem. This positions investors to gain a competitive edge in the dynamic and complex financial markets. Although the FPPO algorithm demonstrates significant potential in trade execution optimization, it may need to integrate a broader range of real-world variables and develop advanced privacy-preserving mechanisms to enhance its applicability in diverse financial contexts. |
| format | Article |
| id | doaj-art-6cbdfb4cd27d41b0a1112b5599a97714 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6cbdfb4cd27d41b0a1112b5599a977142025-02-12T00:01:51ZengIEEEIEEE Access2169-35362025-01-0113250742508610.1109/ACCESS.2025.353885910872909Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information SecurityHaogang Feng0https://orcid.org/0000-0001-9714-9060Yue Wang1https://orcid.org/0000-0002-8526-1300Shida Zhong2https://orcid.org/0000-0003-2330-8166Tao Yuan3https://orcid.org/0000-0002-9525-3814Zhi Quan4https://orcid.org/0000-0001-8108-2893State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaState Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, ChinaState Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, ChinaState Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University, Shenzhen, ChinaStock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an adaptive trade execution framework that leverages joint reinforcement learning. The FPPO algorithm demonstrates significant improvements in model performance across various stocks, with average returns enhanced by 3% to 15%. It also exhibits superior performance in key metrics such as the reward function value, showcasing its effectiveness in different financial contexts. The paper further explores the model’s performance under the FPPO algorithm with varying numbers of client nodes and different risk preferences, underscoring the importance of these factors in model construction. The results substantiate the FPPO algorithm’s capability to safeguard privacy, ensure high performance, and enable the creation of personalized trading models in the optimal trade execution problem. This positions investors to gain a competitive edge in the dynamic and complex financial markets. Although the FPPO algorithm demonstrates significant potential in trade execution optimization, it may need to integrate a broader range of real-world variables and develop advanced privacy-preserving mechanisms to enhance its applicability in diverse financial contexts.https://ieeexplore.ieee.org/document/10872909/Federated reinforcement learningoptimal executionprivacy protection |
| spellingShingle | Haogang Feng Yue Wang Shida Zhong Tao Yuan Zhi Quan Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security IEEE Access Federated reinforcement learning optimal execution privacy protection |
| title | Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security |
| title_full | Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security |
| title_fullStr | Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security |
| title_full_unstemmed | Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security |
| title_short | Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security |
| title_sort | federated reinforcement learning in stock trading execution the fppo algorithm for information security |
| topic | Federated reinforcement learning optimal execution privacy protection |
| url | https://ieeexplore.ieee.org/document/10872909/ |
| work_keys_str_mv | AT haogangfeng federatedreinforcementlearninginstocktradingexecutionthefppoalgorithmforinformationsecurity AT yuewang federatedreinforcementlearninginstocktradingexecutionthefppoalgorithmforinformationsecurity AT shidazhong federatedreinforcementlearninginstocktradingexecutionthefppoalgorithmforinformationsecurity AT taoyuan federatedreinforcementlearninginstocktradingexecutionthefppoalgorithmforinformationsecurity AT zhiquan federatedreinforcementlearninginstocktradingexecutionthefppoalgorithmforinformationsecurity |