Market Making with Deep Reinforcement Learning from Limit Order Books
Hong Guo Jianwu Lin Fanlin Huang
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.