Business rules are crucial for the securities domain and serve as the source of requirements for securities trading systems. Due to the variability of these business rules, how to improve the efficiency of specifying software requirements from business rule trading documents has become a core problem. The securities business rule documents feature numerous software-unrelated descriptions, abundant professional terms, and many context-related expressions and abstract representations, which necessitate the support of domain-specific knowledge for automatic specification. As a result, how to integrate the domain-related knowledge into the automatic process becomes a key problem for specification. This study proposes an automatic specification method for securities domain businesses integrating large language models and the domain knowledge base. It leverages the large language models, employing techniques such as fine-tuning and in-context learning to embed domain knowledge for natural language processing tasks such as rule classification and requirement information extraction. Additionally, this study also employs the domain knowledge base to provide professional knowledge and assist in the operationalization and relationship extraction of requirements. Finally, requirement specification in the form of data flow is formed. The evaluation results show that the proposed approach can process business rule documents in various securities trading fields, achieving an average function point identification rate of 91.97% on the evaluation dataset, which matches or even surpasses the level of experts in the domain, with the efficiency improved by an average of 10 times compared to human participants.