Multi-Factor Financial Architecture: Integrating Near-Real-Time Stream Processing, Large Language Models, and Quantitative Risk Engines for Stock Advisory Systems

This paper formulates an institutional-grade, multi-factor architecture for near-real-time automated stock advisory systems. By synthesizing tick-level market data from Bloomberg infrastructure with deep learning, natural language sentiment processing, and macroeconomic indicators, the proposed system provides regime-aware portfolio allocation signals [1]. We detail a multi-model ensemble combining Transformer decoders, hybrid long short-term memory (LSTM) networks, and gradient-boosted trees. To maintain empirical rigor, the methodology incorporates point-in-time data normalization to prevent look-ahead bias, cross-model LLM verification to mitigate hallucination risks, and fractional Kelly position sizing for risk optimization. Systemic dynamics—including latency constraints and correlated agent risks—are systematically analyzed to provide a clear technical blueprint for implementation and open-source benchmarking. Limited-live PoC available at https://trade.aiagentfolio.com.