Generating Realistic Stock Market Order Streams


  • Junyi Li University of Pittsburgh
  • Xintong Wang University of Michigan
  • Yaoyang Lin Harvard University
  • Arunesh Sinha Singapore Management University
  • Michael Wellman University of Michigan



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.




How to Cite

Li, J., Wang, X., Lin, Y., Sinha, A., & Wellman, M. (2020). Generating Realistic Stock Market Order Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 727-734.



AAAI Technical Track: Applications