Quantitative backtesting platforms serve as "offline decision-making testbeds" for strategy evaluation, parameter exploration, and risk verification. However, traditional backtesting systems suffer from issues such as event sequence drift, similar matching semantics, unstable concurrent scheduling, and backtesting results failing to truly reflect market paths. This results in significant deviations in strategy returns, drawdowns, and transaction details. To address this pain point, this paper presents a technical solution for deterministic replay using an event-driven engine on a quantitative backtesting platform. This framework uses a unified event clock, sequence number adjudication, state snapshots, incremental logs, matching semantic encapsulation, and audit verification chain as its core, mapping changes in market data, orders, transactions, fees, risk control triggers, and account status into a recordable, replayable, and verifiable event stream. Based on publicly available research on event replay benchmark data and statistical results from high-performance event stream systems, the system is designed from four aspects: event modeling, execution consistency, low-latency transmission, and deviation measurement. Furthermore, a backtesting replay consistency metric is constructed. Analysis shows that deterministic replay can not only improve the interpretability and accountability of backtesting results, but also greatly improve the engineering reliability of parameter optimization, regression testing, and strategy pre-deployment verification.

Ren Yan. Research on the Design and Consistency Verification of Deterministic Replay Mechanism for Event-Driven Engines in Quantization Backtesting Platforms. International Journal of Neural Network (2026), Vol. 5, Issue 1: 33-41. https://doi.org/10.38007/NN.2026.050104.

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