The speeds at which transactions are completed in global financial markets are accelerating and, in the process, connecting financial centers around the globe like never before. Algorithmic trading at high frequency is a form of automated trading in which machines, rather than humans, make the decision to buy or sell in spatiotemporal sequences. Insofar as they have agency of their own, their actions support the owners of the means of production. These techniques codevelop with new financial geographies. Accordingly, I examine technological change and speculative time-spaces of algorithmic strategies at stock exchanges. By analyzing algorithmic finance, I examine how—and to what extent—time, speed, location, and distance become critical for algorithmic finance by configuring time-spaces as competitive factors. The analysis interprets time-spaces of high-frequency trading strategies through the ways in which algorithmic finance constititutes what I term mobile market-informational epicenters. This article discusses the spatiotemporalities of market information and examines whether space-times of privately owned high-frequency trading infrastructures result in a juxtaposition between “public” and “private” market information across digital and physical space. It thereby responds to the questions of what role geography plays when algorithms make money in microseconds and how techno-financial time-spaces turn into competitive advantage.

全球金融市场的交易完成速度正在加快，并在此过程中以前所未有的方式连接全球金融中心。高频算法交易是一种自动交易形式，在这种交易中，机器而不是人类，以时空顺序做出买卖决定。只要他们有自己的能动性，他们的行动就会支持生产资料的所有者。这些技术与新的金融领域共同发展。因此，我研究了证券交易所算法策略的技术变革和投机时间空间。通过分析算法金融，我通过将时间空间配置为竞争因素来研究时间、速度、位置和距离如何以及在多大程度上成为算法金融的关键。移动市场信息中心。本文讨论了市场信息的时空性，并检验了私有高频交易基础设施的时空是否会导致跨数字和物理空间的“公共”和“私人”市场信息并列。因此，它回答了当算法在微秒内赚钱时地理扮演什么角色以及技术金融时空如何转化为竞争优势的问题。