Integrating Tick-level Data and Periodical Signal for High-frequency Market Making
This addresses the problem of liquidity provision in complex, data-intensive financial markets for traders and institutions, representing an incremental improvement through hybrid methods.
The paper tackles the challenge of developing effective market making strategies in high-frequency trading by proposing a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals. The results show that this framework outperforms existing methods in profitability and risk management in cryptocurrency market simulations and real data experiments.
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.