A Multi-agent System Based On LLM For Trading Financial Assets
In this paper, we propose an agent-based system including Large Language Models (LLM). It depicts a multi-agent system architecture for cryptocurrency trading, consisting of five distinct agents: an Analyst, a Data Scientist, a Strategy Developer, a Trading Advisor, and a Risk Manager. Each agent...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Ovidius University Press
2025-02-01
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| Series: | Ovidius University Annals: Economic Sciences Series |
| Subjects: | |
| Online Access: | https://stec.univ-ovidius.ro/html/anale/RO/2024i2/Section%205/18.pdf |
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| Summary: | In this paper, we propose an agent-based system including Large Language Models (LLM). It
depicts a multi-agent system architecture for cryptocurrency trading, consisting of five distinct
agents: an Analyst, a Data Scientist, a Strategy Developer, a Trading Advisor, and a Risk Manager.
Each agent performs a specific task using search and scraping tools to gather necessary information.
These agents then collaborate to manage all aspects of the trading process. The OpenAI API is
utilized to generate outputs from the agents. Three trading scenarios are proposed: moderate risk
tolerance with position trading, high risk tolerance with scalping, and low risk tolerance with swing
trading. Experimental results show that each agent effectively contributes to the trading strategy.
For instance, position trading benefits from comprehensive risk analysis and mitigation strategies,
scalping relies on detailed execution plans and risk management protocols, and swing trading
focuses on market trends and regulatory impacts. |
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| ISSN: | 2393-3127 |