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Towards Realistic Market Simulations: a Generative Adversarial Networks Approach

Author

Listed:
  • Andrea Coletta
  • Matteo Prata
  • Michele Conti
  • Emanuele Mercanti
  • Novella Bartolini
  • Aymeric Moulin
  • Svitlana Vyetrenko
  • Tucker Balch

Abstract

Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.

Suggested Citation

  • Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.
    • Handle: RePEc:arx:papers:2110.13287
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    References listed on IDEAS

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    Cited by:

    1. Andrea Coletta & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2022. "Learning to simulate realistic limit order book markets from data as a World Agent," Papers 2210.09897, arXiv.org.
    2. Andrea Coletta & Joseph Jerome & Rahul Savani & Svitlana Vyetrenko, 2023. "Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness," Papers 2306.12806, arXiv.org.
    3. Zacharia Issa & Blanka Horvath & Maud Lemercier & Cristopher Salvi, 2023. "Non-adversarial training of Neural SDEs with signature kernel scores," Papers 2305.16274, arXiv.org.
    4. Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.

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