Adaptive Quantization with Online Distribution Monitoring for Low-Latency Cross-Modal Sentiment Fusion in High-Frequency Trading

Abstract

We propose a novel cross-modal attention module that integrates adaptive quantization with online distribution monitoring for low-latency sentiment fusion in high-frequency trading. The proposed method addresses the fundamental challenge of fusing text, audio, and market microstructure features under sub-millisecond latency constraints, where conventional softmax-based cross-attention incurs prohibitive quadratic complexity and full-precision arithmetic costs. Our core innovation replaces static quantization with a dynamically self-adjusting architecture that tracks streaming statistical moments of attention logits across modalities. An online distribution monitor computes running mean and variance for each logit using exponential moving averages, then derives a concept drift indicator to detect non-stationarity in the logit distribution. When this indicator exceeds a threshold, the module triggers recalibration of a locally-adaptive codebook, where bit-widths are dynamically allocated based on local variance—high-variance logits receive more bits for precise representation, while low-variance logits are coarsely quantized to save computation. The quantized logits then undergo a top-k selection mechanism that retains only the most informative keys per query, thereby reducing complexity to linear order. A temperature-scaled softmax normalizes the selected logits, compensating for quantization noise. Furthermore, we incorporate a semi-supervised density estimation component that maintains an online Gaussian mixture model on the quantized logit space, assigning soft labels to latent sentiment regimes. These pseudo-labels drive a self-supervised contrastive loss that fine-tunes the attention projection matrices without requiring manual annotations. The entire module is implemented on an FPGA using systolic arrays with 8-bit integer arithmetic, achieving a latency of 0.8 microseconds per attention head. Our approach therefore enables robust multimodal sentiment fusion that adapts to evolving market conditions while satisfying the stringent latency budgets of high-frequency trading systems.