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

Author

Listed:
  • Victor Storchan
  • Svitlana Vyetrenko
  • Tucker Balch

Abstract

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.

Suggested Citation

  • Victor Storchan & Svitlana Vyetrenko & Tucker Balch, 2021. "Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators," Papers 2108.00664, arXiv.org.
  • Handle: RePEc:arx:papers:2108.00664
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    References listed on IDEAS

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    1. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
    2. Vince Darley & Alexander V Outkin, 2007. "A NASDAQ Market Simulation:Insights on a Major Market from the Science of Complex Adaptive Systems," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6217, December.
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    Cited by:

    1. Namid R. Stillman & Rory Baggott & Justin Lyon & Jianfei Zhang & Dingqiu Zhu & Tao Chen & Perukrishnen Vytelingum, 2023. "Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks," Papers 2311.11913, arXiv.org, revised Nov 2023.
    2. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.

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