We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.

中文翻译：

---

生成现实的股票市场订单流

我们提出了一种基于生成对抗网络 (GAN) 生成逼真且高保真股市数据的方法。我们的 Stock-GAN 模型采用条件 Wasserstein GAN 来捕获订单的历史依赖性。生成器设计包括特制的方面，包括接近市场拍卖机制的组件，通过订单簿结构增加订单历史以改进生成任务。我们进行消融研究以验证我们网络结构各方面的有用性。我们提供了生成器学习到的分布的数学特征。我们还提出统计数据来衡量生成的订单的质量。我们使用合成和实际市场数据测试我们的方法，与许多基线生成模型进行比较，并发现生成的数据接近真实数据。