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Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response

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  • Svitlana Vyetrenko
  • Shaojie Xu

Abstract

We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interaction by synthesizing market response to our agent's execution decisions from historical data. Due to market impact, executing high volume orders can incur significant cost. We learn trading signals from market microstructure in presence of simulated market response and derive explainable decision-tree-based execution policies using risk-sensitive Q-learning to minimize execution cost subject to constraints on cost variance.

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  • Svitlana Vyetrenko & Shaojie Xu, 2019. "Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response," Papers 1906.02312, arXiv.org, revised Jan 2021.
  • Handle: RePEc:arx:papers:1906.02312
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    File URL: http://arxiv.org/pdf/1906.02312
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    Cited by:

    1. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    2. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.

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