As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert's unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction

翻译：随着算法交易和电子市场持续重塑金融市场的格局，检测并阻止恶意行为者以维护公平高效的市场环境至关重要。大规模数据集的激增与不断变化的交易伎俩使得适应新的市场条件并识别不良行为者变得困难。为此，我们提出一种可灵活适配市场操纵检测领域多种问题的框架。我们的方法首先采用一种标注算法，据此构建训练集以学习弱监督模型，从而识别可能可疑的订单簿状态序列。核心目标是学习一种便于比较未来事件的订单簿表征。随后，我们提出引入专家评估以审查特定的可疑订单簿状态。当专家无法参与时，则通过对已识别可疑订单簿状态应用更复杂的算法作为替代方案。接着，我们在新生成的订单簿表征与专家标注表征之间进行相似性搜索，以对弱学习器的结果进行排序。初步实验结果表明这一方向具有进一步探索的潜力。