Market Making with Deep Reinforcement Learning from Limit Order Books
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Abstract (arXiv)
Market making (MM) is an important research topic in quantitative finance, the agent needs to continuously optimize ask and bid quotes to provide liquidity and make profits. The limit order book (LOB) contains information on all active limit orders, which is an essential basis for decision-making. The modeling of evolving, high-dimensional and low signal-to-noise ratio LOB data is a critical challenge. Traditional MM strategy relied on strong assumptions such as price process, order arrival process, etc. Previous reinforcement learning (RL) works handcrafted market features, which is insufficient to represent the market. This paper proposes a RL agent for market making with LOB data. We leverage a neural network with convolutional filters and attention mechanism (Attn-LOB) for feature extraction from LOB. We design a new continuous action space and a hybrid reward function for the MM task. Finally, we conduct comprehensive experiments on latency and interpretability, showing that our agent has good applicability.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 25 May 2023.
Builds on
15 of 53 references.
References · 15
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- 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
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- 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
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- 2019Model-based Reinforcement Learning for Predictions and Control for Limit Order Bookscited by 14
- 2021Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Unitscited by 10
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Citing papers per year: 2026: 1.
1 paper.