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A Cloud-Native Globally Distributed Financial Exchange Simulator for Studying Real-World Trading-Latency Issues at Planetary Scale

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  • Bradley Miles
  • Dave Cliff

Abstract

We describe a new public-domain open-source simulator of an electronic financial exchange, and of the traders that interact with the exchange, which is a truly distributed and cloud-native system that been designed to run on widely available commercial cloud-computing services, and in which various components can be placed in specified geographic regions around the world, thereby enabling the study of planetary-scale latencies in contemporary automated trading systems. Our simulator allows an exchange server to be launched in the cloud, specifying a particular geographic zone for the cloud hosting service; automated-trading clients which attach to the exchange can then also be launched in the cloud, in the same geographic zone and/or in different zones anywhere else on the planet, and those clients are then subject to the real-world latencies introduced by planetary-scale cloud communication interconnections. In this paper we describe the design and implementation of our simulator, called DBSE, which is based on a previous public-domain simulator, extended in ways that are partly inspired by the architecture of the real-world Jane Street Exchange. DBSE relies fundamentally on UDP and TCP network communications protocols and implements a subset of the FIX de facto standard protocol for financial information exchange. We show results from an example in which the exchange server is remotely launched on a cloud facility located in London (UK), with trader clients running in Ohio (USA) and Sydney (Australia). We close with discussion of how our simulator could be further used to study planetary-scale latency arbitrage in financial markets.

Suggested Citation

  • Bradley Miles & Dave Cliff, 2019. "A Cloud-Native Globally Distributed Financial Exchange Simulator for Studying Real-World Trading-Latency Issues at Planetary Scale," Papers 1909.12926, arXiv.org.
    • Handle: RePEc:arx:papers:1909.12926
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    1. John H. Kagel & Alvin E. Roth, 2016. "The Handbook of Experimental Economics, Volume 2," Economics Books, Princeton University Press, edition 1, volume 2, number 10874.
    2. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    3. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    4. Smith,Vernon L., 2006. "Papers in Experimental Economics," Cambridge Books, Cambridge University Press, number 9780521024655, October.
    5. Steven Gjerstad, 2003. "The Strategic Impact of Pace in Double Auction Bargaining," Microeconomics 0304001, University Library of Munich, Germany.
    6. Steven Gjerstad, 2003. "The Impact of Pace in Double Auction Bargaining," Levine's Bibliography 666156000000000192, UCLA Department of Economics.
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    Cited by:

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    2. Henry Hanifan & John Cartlidge, 2019. "Fools Rush In: Competitive Effects of Reaction Time in Automated Trading," Papers 1912.02775, arXiv.org, revised Nov 2020.

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