论文标题
产生现实的股票市场订单流
Generating Realistic Stock Market Order Streams
论文作者
论文摘要
我们提出了一种基于生成的对抗网络（GAN）生成现实和高保真股票市场数据的方法。我们的股票模型采用有条件的Wasserstein Gan来捕获订单的历史依赖性。发电机设计包括特殊制作的方面，包括近似市场拍卖机制的组件，通过订单书结构来增强订单历史记录以改善生成任务。我们进行一项消融研究，以验证网络结构方面的有用性。我们提供了生成器学到的分布的数学表征。我们还建议统计以衡量生成订单的质量。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相比，发现生成的数据与真实数据接近。
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.
