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Market Simulation under Adverse Selection

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  • Luca Lalor
  • Anatoliy Swishchuk

Abstract

In this paper, we study the effects of fill probabilities and adverse fills on the trading strategy simulation process. We specifically focus on a stochastic optimal control market-making problem and test the strategy on ES (E-mini S&P 500), NQ (E-mini Nasdaq 100), CL (Crude Oil) and ZN (10-Year Treasury Note), which are some of the most liquid futures contract listed on the CME (Chicago Mercantile Exchange). We provide empirical evidence which shows how fill probabilities and adverse fills can significantly effect performance, and propose a more prudent simulation framework for dealing with this. Many previous works aim to measure different types of adverse selection in the limit order book, however, they often simulate price processes and market orders independently. This has the ability to largely inflate the performance of a short-term style trading strategy. Our studies show that using more realistic fill probabilities, and tracking adverse fills, in the strategy simulation process, more accurately portrays how these types of trading strategies would perform in reality.

Suggested Citation

  • Luca Lalor & Anatoliy Swishchuk, 2024. "Market Simulation under Adverse Selection," Papers 2409.12721, arXiv.org.
    • Handle: RePEc:arx:papers:2409.12721
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    References listed on IDEAS

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    1. Ana Roldan Contreras & Anatoliy Swishchuk, 2022. "Optimal Liquidation, Acquisition and Market Making Problems in HFT under Hawkes Models for LOB," Risks, MDPI, vol. 10(8), pages 1-32, August.
    2. 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.
    3. Álvaro Cartea & Ryan Donnelly & Sebastian Jaimungal, 2018. "Enhancing trading strategies with order book signals," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 1-35, January.
    4. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    5. Costis Maglaras & Ciamac C. Moallemi & Muye Wang, 2022. "A deep learning approach to estimating fill probabilities in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 1989-2003, November.
    6. Brian Bulthuis & Julio Concha & Tim Leung & Brian Ward, 2017. "Optimal execution of limit and market orders with trade director, speed limiter, and fill uncertainty," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-29, June.
    7. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    8. Álvaro Arroyo & Álvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2024. "Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers," Quantitative Finance, Taylor & Francis Journals, vol. 24(1), pages 35-57, January.
    9. Rama Cont & Adrien de Larrard, 2013. "Price Dynamics in a Markovian Limit Order Market," Post-Print hal-00552252, HAL.
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    Cited by:

    1. Luca Lalor & Anatoliy Swishchuk, 2024. "Reinforcement Learning in Non-Markov Market-Making," Papers 2410.14504, arXiv.org, revised Nov 2024.

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