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Double Deep Q-Learning for Optimal Execution

Author

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  • Brian Ning
  • Franco Ho Ting Lin
  • Sebastian Jaimungal

Abstract

Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader. The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action. We apply our model to nine different stocks and find that it outperforms the standard benchmark approach on most stocks using the measures of (i) mean and median out-performance, (ii) probability of out-performance, and (iii) gain-loss ratios.

Suggested Citation

  • Brian Ning & Franco Ho Ting Lin & Sebastian Jaimungal, 2021. "Double Deep Q-Learning for Optimal Execution," Applied Mathematical Finance, Taylor & Francis Journals, vol. 28(4), pages 361-380, July.
    • Handle: RePEc:taf:apmtfi:v:28:y:2021:i:4:p:361-380
    DOI: 10.1080/1350486X.2022.2077783
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    Citations

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    Cited by:

    1. Soohan Kim & Jimyeong Kim & Hong Kee Sul & Youngjoon Hong, 2023. "An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution," Papers 2307.10649, arXiv.org.
    2. Xianhua Peng & Chenyin Gong & Xue Dong He, 2023. "Reinforcement Learning for Financial Index Tracking," Papers 2308.02820, arXiv.org, revised Nov 2024.
    3. Kerndler, Martin, 2023. "Occupational safety in a frictional labor market," Labour Economics, Elsevier, vol. 83(C).
    4. Marcello Monga, 2024. "Automated Market Making and Decentralized Finance," Papers 2407.16885, arXiv.org.
    5. Alexandre Carbonneau & Frédéric Godin, 2023. "Deep Equal Risk Pricing of Financial Derivatives with Non-Translation Invariant Risk Measures," Risks, MDPI, vol. 11(8), pages 1-27, August.

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