Detecting and Triaging Spoofing using Temporal Convolutional Networks

arXiv:2403.13429v1 Announce Type: cross
Abstract: 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 …

abstract adapt agents arxiv cs.ce cs.lg datasets electronic fair financial financial markets landscape large datasets marketplace markets networks q-fin.cp q-fin.gn q-fin.tr rogue temporal trade trading tricks type