The effective construction of an Algorithmic Trading (AT) strategy often relies on market simulators, which remains challenging due to existing methods' inability to adapt to the sequential and dynamic nature of trading activities. This work fills this gap by proposing a metric to quantify market discrepancy. This metric measures the difference between a causal effect from underlying market unique characteristics and it is evaluated through the interaction between the AT agent and the market. Most importantly, we introduce Algorithmic Trading-guided Market Simulation (ATMS) by optimizing our proposed metric. Inspired by SeqGAN, ATMS formulates the simulator as a stochastic policy in reinforcement learning (RL) to account for the sequential nature of trading. Moreover, ATMS utilizes the policy gradient update to bypass differentiating the proposed metric, which involves non-differentiable operations such as order deletion from the market. Through extensive experiments on semi-real market data, we demonstrate the effectiveness of our metric and show that ATMS generates market data with improved similarity to reality compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network (cWGAN) approach. Furthermore, ATMS produces market data with more balanced BUY and SELL volumes, mitigating the bias of the cWGAN baseline approach, where a simple strategy can exploit the BUY/SELL imbalance for profit.

提出了一种度量市场差异的指标，并通过优化该指标引入了基于算法交易的市场模拟（ATMS），通过强化学习中的随机策略建模来考虑交易的时序特性，通过实验证明了该指标的有效性，并展示了ATMS相比基于条件Wasserstein生成对抗网络（cWGAN）的方法生成更接近真实数据的市场数据，同时产生更平衡的买卖交易量，减少了买卖交易不均衡的偏差。

ATMS: 算法交易引导的市场模拟