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Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems

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  • Zhen Zhang
  • Dave Cliff

Abstract

Financial markets populated by human traders often exhibit "market impact", where the traders' quote-prices move in the direction of anticipated change, before any transaction has taken place, as an immediate reaction to the arrival of a large (i.e., "block") buy or sell order in the market: e.g., traders in the market know that a block buy order will push the price up, and so they immediately adjust their quote-prices upwards. Most major financial markets now involve many "robot traders", autonomous adaptive software agents, rather than humans. This paper explores how to give such trader-agents a reliable anticipatory sensitivity to block orders, such that markets populated entirely by robot traders also show market-impact effects. In a 2019 publication Church & Cliff presented initial results from a simple deterministic robot trader, ISHV, which exhibits this market impact effect via monitoring a metric of imbalance between supply and demand in the market. The novel contributions of our paper are: (a) we critique the methods used by Church & Cliff, revealing them to be weak, and argue that a more robust measure of imbalance is required; (b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al., 2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we demonstrate the use of the more robust MLOFI measure in extending ISHV, and also the well-known AA and ZIP trading-agent algorithms (which have both been previously shown to consistently outperform human traders). We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff. The source-code for our work reported here is freely available on GitHub.

Suggested Citation

  • Zhen Zhang & Dave Cliff, 2020. "Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems," Papers 2012.12555, arXiv.org.
    • Handle: RePEc:arx:papers:2012.12555
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    References listed on IDEAS

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    1. Aaron Wray & Matthew Meades & Dave Cliff, 2020. "Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data," Papers 2012.00821, arXiv.org.
    2. Steven Gjerstad, 2003. "The Strategic Impact of Pace in Double Auction Bargaining," Microeconomics 0304001, University Library of Munich, Germany.
    3. Arthur le Calvez & Dave Cliff, 2018. "Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market," Papers 1811.02880, arXiv.org.
    4. Steven Gjerstad, 2003. "The Impact of Pace in Double Auction Bargaining," Levine's Bibliography 666156000000000192, UCLA Department of Economics.
    5. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2014. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88.
    6. Ke Xu & Martin D. Gould & Sam D. Howison, 2019. "Multi-Level Order-Flow Imbalance in a Limit Order Book," Papers 1907.06230, arXiv.org, revised Oct 2019.
    7. Petrescu, Monica & Wedow, Michael, 2017. "Dark pools in European equity markets: emergence, competition and implications," Occasional Paper Series 193, European Central Bank.
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