Financial trading analytics increasingly demands modular, explainable, and adaptive intelligence systems capable of handling volatile market conditions and multimodal data streams. Recent advances in generative Artificial Intelligence (AI), Large Language Models (LLMs), and graph-based representations have enabled the creation of intelligent agents that can reason over complex, interconnected financial data. We introduce GWise, a graph-structured, generative AI-enabled multi-agent framework for real-time financial trading analytics delivered via secure web services. GWise models financial decision making as a directed computational graph of specialized analytical agents, including technical, fundamental, sentiment, and risk analysis crews, whose outputs are orchestrated through a memory-augmented LLM. This graph-structured design enables transparent, adaptive, and explainable trade recommendations that evolve over time. We demonstrate how the framework's agent orchestration forms a dynamic service graph, facilitating composability, fault isolation, and scalable deployment through cloud-native APIs. Extensive back-testing and simulated market conditions show that GWise outperforms traditional strategies in risk-adjusted returns while offering improved interpretability and service robustness. Our work illustrates how graph-based multiagent coordination and generative reasoning can advance real-time financial analytics as a service.