让生成的金融数据不仅看起来真，还能经得起实盘回测考验。
Beyond Visual Realism: Toward Reliable Financial Time Series Generation
- 将典型金融特征转为可微约束，联合对抗损失优化
- 在上证综指数据上实现稳定回测表现，基线模型常崩溃
- 适合追求真实可用合成数据的量化研究者
金融时间序列生成模型常生成外观逼真、符合肥尾等典型特征的数据，但实际回测中频繁失效，如GAN或WGAN-GP常因崩溃产生极端不实结果。我们发现根源在于忽视市场不对称性与罕见尾部事件，而这些对风险影响重大。为此提出结构化事实对齐GAN（SFAG），将关键典型特征转化为可微结构约束，与对抗损失联合优化。在2004–2024年上证综指数据上的实验表明，基准GAN生成数据回测不稳定且不现实，而SFAG生成数据既保持典型特征，又能支撑稳健动量策略表现。结果表明，结构保持目标对弥合表面真实与实际可用之间的鸿沟至关重要。
原文摘要 · Abstract (English)
Generative models for financial time series often create data that look realistic and even reproduce stylized facts such as fat tails or volatility clustering. However, these apparent successes break down under trading backtests: models like GANs or WGAN-GP frequently collapse, yielding extreme and unrealistic results that make the synthetic data unusable in practice. We identify the root cause in the neglect of financial asymmetry and rare tail events, which strongly affect market risk but are often overlooked by objectives focusing on distribution matching. To address this, we introduce the Stylized Facts Alignment GAN (SFAG), which converts key stylized facts into differentiable structural constraints and jointly optimizes them with adversarial loss. This multi-constraint design ensures that generated series remain aligned with market dynamics not only in plots but also in backtesting. Experiments on the Shanghai Composite Index (2004--2024) show that while baseline GANs produce unstable and implausible trading outcomes, SFAG generates synthetic data that preserve stylized facts and support robust momentum strategy performance. Our results highlight that structure-preserving objectives are essential to bridge the gap between superficial realism and practical usability in financial generative modeling.
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