Deep Recurrent Q-Networks for Market Making

Pankaj Kumar

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

Market Making is high frequency trading strategy in which an agent provides liquidity simultaneously quoting a bid price and an ask price on an asset. Market Makers reaps profits in the form of the spread between the quoted price placed on the buy and sell prices. Due
to complexity in inventory risk, counterparties to trades and information asymmetry, understating of market making algorithms is relatively unexplored by academicians across disciple. In this paper, we develop realistic simulations of limit order markets and use it to design a market making agent using Deep Recurrent Q-Networks. Our approach outperforms
a prominent benchmark strategy from literature, which uses temporal-difference reinforcement learning to design market maker agents. The agents successfully reproduce stylized facts in historical trade data from each simulation.
Original languageEnglish
Publication date2020
Number of pages10
Publication statusPublished - 2020
EventThe Thirteenth Conference on Artificial General Intelligence. AGI-20 Virtual Conference -
Duration: 22 Jun 202026 Jun 2020
Conference number: 13
http://agi-conf.org/2020/

Conference

ConferenceThe Thirteenth Conference on Artificial General Intelligence. AGI-20 Virtual Conference
Number13
Period22/06/202026/06/2020
Internet address

Keywords

  • Deep reinforcement learning
  • Market making
  • Limit order books
  • High frequency trading strategies
  • Agent based models

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