Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable Symbolics), a novel neuro-symbolic framework combining a continuous-time Laplace Neural Operator encoder, a neural stochastic differential equation regularised by physics-informed losses, and a differentiable symbolic bottleneck that distils interpretable trading rules. The model enforces economic plausibility via two novel regularisation terms: a Feynman-Kac PDE residual penalising local no-arbitrage violations, and a market price of risk penalty bounding the instantaneous Sharpe ratio. We evaluate ARTEMIS against six strong baselines on four datasets: Jane Street, Optiver, Time-IMM, and DSLOB (a synthetic crash regime). Results demonstrate ARTEMIS achieves state-of-the-art directional accuracy, outperforming all baselines on DSLOB (64.96%) and Time-IMM (96.0%). A comprehensive ablation study confirms each component's contribution: removing the PDE loss reduces directional accuracy from 64.89% to 50.32%. Underperformance on Optiver is attributed to its long sequence length and volatility-focused target. By providing interpretable, economically grounded predictions, ARTEMIS bridges the gap between deep learning's power and the transparency demanded in quantitative finance.

翻译：定量金融中的深度学习模型通常作为黑箱运行，缺乏可解释性，且未能融入无套利约束等基本经济原理。本文提出ARTEMIS（通过经济模型与可解释符号实现的无套利表征），这是一种新颖的神经符号框架，结合了连续时间拉普拉斯神经算子编码器、受物理信息损失正则化的神经随机微分方程，以及一个可提炼可解释交易规则的可微分符号瓶颈。该模型通过两种新颖的正则化项强制执行经济合理性：惩罚局部无套利违反的费曼-卡茨偏微分方程残差，以及限制瞬时夏普比率的风险市场价格惩罚。我们在四个数据集（Jane Street、Optiver、Time-IMM和DSLOB，一个合成市场崩盘情境）上，将ARTEMIS与六个强基线模型进行了评估。结果表明，ARTEMIS实现了最先进的定向准确率，在DSLOB（64.96%）和Time-IMM（96.0%）上优于所有基线。全面的消融研究证实了每个组件的贡献：去除PDE损失使定向准确率从64.89%降至50.32%。在Optiver上的欠佳表现归因于其长序列长度和以波动率为中心的目标。通过提供可解释、基于经济依据的预测，ARTEMIS弥合了深度学习的强大能力与定量金融所要求的透明度之间的差距。