In this chapter I focus on the changes in algorithmic trading in financial markets
brought about by developments in machine learning and artificial intelligence (AI).
Financial trading has for a long time been dominated by highly sophisticated forms of data processing and computation in the dominance of the “quants”. Yet over the last two decades high-frequency trading (HFT), as a form of automated, algorithmic trading focused on speed and volume rather than smartness, has dominated the arms race in financial markets. I want to suggest that machine learning and AI are today changing the cognitive parameters of this arms race, shifting the boundaries between “dumb” algorithms in HFT and “smart” algorithms in other forms of algorithmic trading. Whereas HFT is largely focused on data internal and dynamics endemic to financial markets, new forms of algorithmic trading enabled by AI are enlarging the ecology of financial markets through ways in which automated trading draws on a wider set of data such as social data for analytics such as sentiment analysis. I want to suggest that to understand the politics of these shifts it is insightful to focus on cognition as a battleground in financial markets, with AI and machine learning leading to a further redistribution and new temporalities of cognition. A politics of cognition must grapple with the opacities and temporalities of algorithmic trading in financial markets, which constitute limits to the democratization of finance
as well as its social regulation.