跳至正文
返回文库全部文档

多智能体市场模拟器的历史状态校准

文章 arXiv papers · 作者: Victor Storchan et al.
阅读原文

总结

本文讨论如何调整多智能体市场模拟器，使其生成的价格和成交量序列接近历史市场状况。这类模拟器可用于交易策略测试。作者强调，模拟器需要呈现普通时期和市场承压阶段，包括 COVID 疫情初期观察到的状况。

所提出的方法首先在自注意力 GAN 中训练一个判别器，以区分真实与模拟的价格和成交量序列。随后，在优化框架中使用该判别器，调整一个智能体类型已知的模拟器，以匹配指定的市场情景。论文报告的实验展示了该方法的有效性。现有描述没有提供性能指标、基准比较，也未说明具体模拟器和数据，因此无法确定这种校准方法能在多大范围内推广到不同市场或状态。

核心观点

  • 该方法校准多智能体模拟器，以重现价格和成交量数据中的历史市场状态。
  • 训练自注意力 GAN 判别器，以区分观测序列和模拟序列。
  • 优化步骤利用判别器，为智能体类型已知的模拟器调整参数。
  • 作者报告实验结果有效，但摘要没有提供指标或泛化分析。

标签

机器学习市场微观结构回测风险管理
全文
# 2108.00664


# Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators









In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test trading strategies before deploying them to real-time trading, multi-agent market environments calibrated so that the time series that result from interaction of simulated agents resemble historical are often used. To ensure adequate testing, one must test trading strategies in a variety of market scenarios -- which includes both scenarios that represent ordinary market days as well as stressed markets (most recently observed due to the beginning of COVID pandemic). In this paper, we address the problem of multi-agent simulator parameter calibration to allow simulator capture characteristics of different market regimes. We propose a novel two-step method to train a discriminator that is able to distinguish between "real" and "fake" price and volume time series as a part of GAN with self-attention, and then utilize it within an optimization framework to tune parameters of a simulator model with known agent archetypes to represent a market scenario. We conclude with experimental results that demonstrate effectiveness of our method.

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。