A Unified Cloud-Edge Infrastructure for High-Frequency Financial Forecasting: Synergizing Stream Computing with LLM-Based Reasoning
Abstract
The integration of Large Language Models (LLMs) into the high-frequency financial forecasting domain represents a significant shift from purely numerical autoregressive models to multimodal, reasoning-based systems. However, the computational intensity of LLMs often conflicts with the millisecond-level latency requirements of modern capital markets. This paper introduces a unified cloud-edge infrastructure designed to harmonize high-speed stream computing with the cognitive reasoning capabilities of LLMs. We propose a hierarchical system architecture that offloads time-critical numerical processing to the edge while utilizing a cloud-based reasoning engine for contextual and geopolitical synthesis. By employing a dynamic orchestration layer, the system manages the inherent trade-offs between inference throughput and analytical depth. We provide a rigorous examination of system-level considerations, including deployment strategies, environmental sustainability, and the socio-technical implications of autonomous financial agents. The discussion extends to the governance frameworks necessary to maintain market integrity and the policy implications of deploying such complex infrastructures in a global economic context. Our findings suggest that the synergy of stream computing and agentic reasoning provides a more robust and adaptive forecasting mechanism than traditional quantitative methods, provided that the underlying infrastructure is designed with a focus on low-latency synchronization and hardware-level security. This research offers a comprehensive roadmap for the next generation of financial forecasting systems that prioritize both speed and contextual intelligence.
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