Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2024
Abstract
Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-frequency trading with various assets. While advanced AI techniques like deep learning and reinforcement learning are extensively utilized in finance, their application in financial trading tasks often faces challenges due to inadequate handling of multimodal data and limited generalizability across various tasks. To address these challenges, we present FinAgent, a multimodal foundational agent with tool augmentation for financial trading. FinAgent's market intelligence module processes a diverse range of data-numerical, textual, and visual-to accurately analyze the financial market. Its unique dual-level reflection module not only enables rapid adaptation to market dynamics but also incorporates a diversified memory retrieval system, enhancing the agent's ability to learn from historical data and improve decision-making processes. The agent's emphasis on reasoning for actions fosters trust in its financial decisions. Moreover, FinAgent integrates established trading strategies and expert insights, ensuring that its trading approaches are both data-driven and rooted in sound financial principles. With comprehensive experiments on 6 financial datasets, including stocks and Crypto, FinAgent significantly outperforms 12 state-of-the-art baselines in terms of 6 financial metrics with over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39% relative improvement) is achieved on one dataset. Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
Keywords
Large Language Models, Quantitative trading, Financial AI agents, Data mining, Machine learning, Electronic commerce
Discipline
Artificial Intelligence and Robotics | Management Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024) : Barcelona, Spain, August 25-29
First Page
4314
Last Page
4325
Identifier
10.1145/3637528.3671801
Publisher
Association for Computing Machinery
City or Country
New York
Citation
ZHANG, Wentao; ZHAO, Lingxuan; XIA, Haochong; SUN, Shuo; SUN, Jiaze; QIN, Molei; LI, Xinyi; ZHAO, Yuqing; ZHAO, Yilei; CAI, Xinyu; ZHENG, Longtao; Xinrun WANG; and AN, Bo.
A multimodal foundation agent for financial trading : Tool-augmented, diversified, and generalist. (2024). Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024) : Barcelona, Spain, August 25-29. 4314-4325.
Available at: https://ink.library.smu.edu.sg/sis_research/9830
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3637528.3671801
Comments
PDF provided by faculty