In this paper, we introduce DeepTraderX (DTX), a simple Deep Learning-based trader, and present results that demonstrate its performance in a multi-threaded market simulation. In a total of about 500 simulated market days, DTX has learned solely by watching the prices that other strategies produce. By doing this, it has successfully created a mapping from market data to quotes, either bid or ask orders, to place for an asset. Trained on historical Level-2 market data, i.e., the Limit Order Book (LOB) for specific tradable assets, DTX processes the market state $S$ at each timestep $T$ to determine a price $P$ for market orders. The market data used in both training and testing was generated from unique market schedules based on real historic stock market data. DTX was tested extensively against the best strategies in the literature, with its results validated by statistical analysis. Our findings underscore DTX's capability to rival, and in many instances, surpass, the performance of public-domain traders, including those that outclass human traders, emphasising the efficiency of simple models, as this is required to succeed in intricate multi-threaded simulations. This highlights the potential of leveraging "black-box" Deep Learning systems to create more efficient financial markets.

DeepTraderX（DTX）是一种简单的基于深度学习的交易机器人，通过观察其他策略产生的价格，以及利用历史的Level-2市场数据，成功地将市场数据映射到报价并放置资产的买卖单，其结果经过统计分析验证，显示了简单模型的高效性，强调潜在利用“黑盒”深度学习系统创造更高效的金融市场。

DeepTraderX: 用深度学习在多线程市场模拟中挑战传统交易策略