FinRobot's four-tier professional architecture
FinRobot employs a four-layer architectural design optimized specifically for financial AI tasks. The top layer is the financial AI agent layer, which contains a variety of specialized agents such as market prediction agents, document analysis agents, trading strategy agents, etc., and supports the Chain of Thought (CoT) prompting methodology specific to the financial domain. The second layer is the financial LLM algorithm layer, which focuses on the tuning of large-scale language models specific to the financial field, significantly enhancing the professionalism of financial analysis. The third layer, LLMOps & DataOps layer, is responsible for multi-source data integration and dynamic adaptation of multiple LLM models. The bottom layer is the multi-source LLM base model layer, which adopts the plug-and-play LLM model invocation method and can be flexibly adapted to different financial analysis tasks.
Advantages of Architectural Design
This layered architectural design brings multiple advantages:
- Clear responsibilities at all levels to ensure efficient operation of the system
- Financial expertise is deeply integrated into the design of each floor
- The system is much more scalable and adaptable
- Significantly more efficient execution of specific financial tasks
With this specialized architectural design, FinRobot is able to excel in complex financial analysis scenarios, far exceeding the performance of general-purpose AI tools.
This answer comes from the articleFinRobot: An Intelligent Body to Improve Financial Data Analysis Efficiency and Investment ResearchThe
