Integrating Tick-level Data and Periodical Signal for High-frequency
  Market Making
Jiafa He Cong Zheng Can Yang
We focus on the problem of market making in high-frequency trading. Market
making is a critical function in financial markets that involves providing
liquidity by buying and selling assets. However, the increasing complexity of
financial markets and the high volume of data generated by tick-level trading
makes it challenging to develop effective market making strategies. To address
this challenge, we propose a deep reinforcement learning approach that fuses
tick-level data with periodic prediction signals to develop a more accurate and
robust market making strategy. Our results of market making strategies based on
different deep reinforcement learning algorithms under the simulation scenarios
and real data experiments in the cryptocurrency markets show that the proposed
framework outperforms existing methods in terms of profitability and risk
management.