We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simulation, but the majority of this work focuses on pricing strategy and does not consider the time taken for these strategies to compute. In real-world financial markets, speed is known to heavily influence the design of automated trading algorithms, with the generally accepted wisdom that faster is better. Here, we introduce increasingly realistic models of trading speed and profile the computation times of a suite of eminent trading algorithms from the literature. Results demonstrate that: (a) trading performance is impacted by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA) algorithm, until recently considered the most dominant trading strategy in the literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave one tick off the current best bid or ask - when relative computation times are accurately simulated.

翻译：我们探索模拟金融市场自动交易战略反应时间的竞争性效应,这些反应时间含有单一交换公共限制单册和连续的双重拍卖比对。数十年来进行的大量研究都致力于贸易代理设计和模拟,但大部分工作侧重于定价战略,没有考虑这些战略的计算时间。在现实世界的金融市场,已知速度严重影响了自动化交易算法的设计,普遍接受的智慧更快。在这里,我们引入了日益现实的贸易速度模式,并描绘了文献中一套著名交易算法的计算时间。结果显示:(a) 贸易业绩受到速度的影响,但速度并不总是更好;(b) 适应-侵略(AA)算法,直到最近考虑文献中最占主导地位的贸易战略之前,被简单化的Shaver(SHVR)战略(SHVR)所超越了。当相对的计算时间被精确模拟时,它比目前最佳的出价或问问(如果相对的计算时间准确的话)要快得多。