Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization

• URL: http://arxiv.org/abs/2112.03874v1
• Date: Fri, 3 Dec 2021 22:57:46 GMT
• Title: Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization
• Authors: Yuanlu Bai, Henry Lam, Svitlana Vyetrenko, Tucker Balch
• Abstract summary: Multi-agent market simulation is commonly used to create an environment for downstream machine learning or reinforcement learning tasks. We propose a simple and efficient framework for calibrating multi-agent market simulator parameters from historical time series observations.
• Score: 2.6749843984691672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent market simulation is commonly used to create an environment for downstream machine learning or reinforcement learning tasks, such as training or testing trading strategies before deploying them to real-time trading. In electronic trading markets only the price or volume time series, that result from interaction of multiple market participants, are typically directly observable. Therefore, multi-agent market environments need to be calibrated so that the time series that result from interaction of simulated agents resemble historical -- which amounts to solving a highly complex large-scale optimization problem. In this paper, we propose a simple and efficient framework for calibrating multi-agent market simulator parameters from historical time series observations. First, we consider a novel concept of eligibility set to bypass the potential non-identifiability issue. Second, we generalize the two-sample Kolmogorov-Smirnov (K-S) test with Bonferroni correction to test the similarity between two high-dimensional time series distributions, which gives a simple yet effective distance metric between the time series sample sets. Third, we suggest using Bayesian optimization (BO) and trust-region BO (TuRBO) to minimize the aforementioned distance metric. Finally, we demonstrate the efficiency of our framework using numerical experiments.

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