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.

翻译：算法交易（AT）策略的有效构建通常依赖于市场模拟器，但由于现有方法无法适应交易活动的序列性和动态性，这仍然是具有挑战性的。本研究通过提出一种量化市场差异性的指标来填补这一空白。该指标衡量由市场独特特征产生的因果效应差异，并通过AT智能体与市场的交互进行评估。最重要的是，我们通过优化所提出的指标，引入了算法交易引导的市场模拟（ATMS）。受SeqGAN启发，ATMS将模拟器形式化为强化学习（RL）中的随机策略，以解决交易的序列性问题。此外，ATMS利用策略梯度更新来避免对提出的指标进行微分，该指标涉及不可微分操作（如从市场中删除订单）。通过在半真实市场数据上进行的大量实验，我们验证了该指标的有效性，并表明与当前最优的条件Wasserstein生成对抗网络（cWGAN）方法相比，ATMS生成的市场数据与真实数据的相似度更高。此外，ATMS生成的市场数据具有更均衡的买入和卖出量，从而减轻了cWGAN基线方法的偏差——在该方法中，简单策略即可利用买卖不平衡来获利。