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用条件生成对抗网络模拟具有响应性的市场

文章 arXiv papers · 作者: Andrea Coletta et al.
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总结

本文指出历史回放回测的一项局限：它们无法模拟市场对策略操作的反应。文章介绍多智能体模拟，以呈现采用不同策略的交易者之间的互动，从而在模拟市场中测试实验性交易智能体。实际难点在于，关于个体交易者的详细历史数据通常属于专有数据或无法获取，因此难以进行真实校准。

所提方法使用汇总历史数据训练条件生成对抗网络，创建一个合成的“世界”智能体，使其能够响应实验性智能体并生成订单。生成器集成于 ABIDES 金融市场模拟器。作者报告称，在旨在反映市场响应性和真实度的风格化特征方面，该方法在大量模拟中优于以往研究。描述未指出具体特征、数据集、市场或表现指标；仅凭模拟结果，无法证明生成的环境能够预测实盘市场行为。

核心观点

  • 历史回放回测无法模拟市场对策略订单的反应。
  • 多智能体模拟可以呈现采用不同策略的交易者之间的互动。
  • 该方法使用汇总历史数据训练条件生成对抗网络。
  • 合成世界智能体会响应实验性交易智能体并生成订单。
  • 该系统集成于 ABIDES，据称改善了与真实度相关的风格化特征。

标签

机器学习回测市场微观结构交易执行统计学
全文
# Towards Realistic Market Simulations: a Generative Adversarial Networks Approach


# Towards Realistic Market Simulations: a Generative Adversarial Networks Approach









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。