Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities across a wide array of natural language processing tasks. However, their potential for solving complex financial trading-related problems has been largely unexplored, primarily due to the scarcity of publicly available proprietary data and the inherently noisy nature of financial textual information. In this talk, we present how we leverage LLMs to address these challenges, offering advanced assistance in extracting insights from diverse financial documents, interpreting market information, and guiding trading judgments. We will explore potential methodologies, model adaptations, and unique strategies to harness LLMs' strengths in overcoming the ambiguities of financial text data, ultimately contributing to more informed and strategic trading decisions.