## 1 Introduction  
As global markets evolve toward increasingly high-frequency and complex structures, the demands for faster and more intelligent decision-making systems continue to rise [1]. In particular, in high-frequency trading (HFT) scenarios, where microsecond-level responsiveness and execution are critical, the ability to capture and act on subtle patterns in real time has become essential for gaining competitive advantages [2]. Against this backdrop, quantitative modeling has gradually shifted from traditional rule-based and linear factor approaches to data-driven and nonlinear paradigms [3]. Meanwhile, the underlying signal data exhibit strong nonlinearity, multi-scale behaviors, and structural instability, presenting significant challenges for classical modeling techniques in terms of both expressiveness and risk control [4]. Conventional approaches typically rely on statistical regression, time series models, and handcrafted rules--such as multi-factor framework, trend-following, or mean-reversion strategies [5]. While these methods have proven useful in relatively stable environments, they exhibit clear limitations when applied to large-scale, high-frequency data streams [6]. Firstly, traditional models often fail to capture nonlinear dependencies and complex interactions within the data, resulting in limited generalization and robustness [7]. Secondly, these methods lack the capacity to model dynamic, structured relationships across heterogeneous signals, as such dependencies are difficult to capture usinglinear techniques [8]. Finally, under conditions of macroeconomic volatility, conventional strategies often lack adaptive mechanisms and are unable to respond swiftly to external perturbations [9]. In recent years, the emergence of deep learning has opened new avenues for data-driven modeling. As a powerful class of nonlinear function approximators, deep neural networks (DNNs) have achieved remarkable success in image recognition, natural language processing, and other complex tasks. Their capacity to learn high-dimensional representations and capture intricate patterns has made them increasingly relevant for prediction, control, and decision-making under uncertainty [10]. Architectures such as LSTM, Transformer, and CNN have been widely adopted in domains such as trend prediction, sentiment analysis, signal generation, and portfolio optimization [11]. For instance, [PERSON] et al. [12] introduced FinRL, an open-source reinforcement learning framework that integrates data handling, environment simulation, training, and evaluation, enabling rapid deployment of RL-based trading strategies. [PERSON] et al. [13] proposed a DNN-based calibration approach to efficiently map observed data to model parameters, significantly accelerating complex pricing tasks. Similarly, [PERSON] et al. [14] developed a deep learning framework combining Correlation-aware Convolutional Neural Networks (CCNNs) and LSTM to enhance the accuracy of risk prediction. However, despite their advantages in short-term forecasting, sequence-based models often struggle to capture the broader structural context within multi-agent or multi-asset systems. They generally lack a global modeling perspective, particularly when asset interconnections evolve dynamically. Furthermore, recent research and practice have increasingly emphasized the role of macroeconomic factors in shaping system-wide dynamics. Both structured indicators--such as interest rates, inflation, and GDP growth--and unstructured macro signals--like policy narratives, geopolitical events, and central bank communication--have a profound impact on market expectations and behavioral patterns. Traditional modeling approaches typically fall short in systematically integrating such diverse macro-level influences, especially in handling unstructured data.