High-fidelity, scalable market simulation is a key instrument for mechanism evaluation, stress testing, and counterfactual policy analysis. Yet existing simulators rarely achieve \emph{mechanism fidelity} beyond single-asset intraday settings, \emph{microstructure fidelity} against historical limit order books (LOB), and \emph{computational tractability} at market scale in a single system. This paper presents \textit{EvoMarket}, a discrete-event, multi-agent financial market simulator designed for intervention-oriented experiments in multi-asset and cross-day environments. EvoMarket couples a high-throughput execution core (optimized LOB data structures, hierarchical scheduling under propagation delays, and asynchronous per-asset matching) with explicit institutional mechanisms (market calendars, opening call auctions, price limits, and T+1 settlement). To avoid expensive black-box calibration, EvoMarket introduces an Oracle-guided in-run self-calibration mechanism that interprets microstructure discrepancy as missing order flow and synthesizes corrective orders at recording checkpoints. Experiments on China A-share order-flow and LOB data show close replay alignment over five trading days, fidelity gains from budgeted in-run calibration across depth levels, broad agent order-space coverage, and scalable performance under increasing input order rates and market breadth. We further demonstrate cross-asset linkage and event-study style intervention evaluation that produces structured dependence and interpretable event-time responses.

翻译：高保真度、可扩展的市场模拟是机制评估、压力测试及反事实政策分析的关键工具。然而，现有模拟器鲜有在单一系统中同时实现：超越单一资产日内设置下“机制保真度”、基于历史限价订单簿的“微观结构保真度”、以及市场尺度下“计算可解性”。本文提出EvoMarket——一种专为多资产跨日环境下干预导向实验设计的离散事件多智能体金融市场模拟器。EvoMarket将高通量执行核心（优化的LOB数据结构、传播延迟下分层调度、异步逐资产撮合）与显式制度机制（交易日历、开盘集合竞价、涨跌幅限制及T+1清算）耦合。为避免昂贵的黑箱校准，EvoMarket引入一种预言机引导的运行时自校准机制：将微观结构偏差解释为缺失订单流，并在记录检查点合成修正订单。基于中国A股订单流与LOB数据的实验表明：该模拟器在五个交易日中实现了紧密的重放对齐、跨深度层级通过预算约束的运行时校准带来保真度增益、广泛的智能体订单空间覆盖，以及在输入订单率与市场宽度递增条件下呈现的可扩展性能。我们进一步展示了跨资产联动性与事件研究式干预评估，可产生结构化依赖关系及可解释的事件时间响应。