Integrating Tick-level Data and Periodical Signal for High-frequency Market Making

TL;DR

This paper introduces a deep reinforcement learning framework that combines tick-level data with periodic signals to enhance high-frequency market making strategies, demonstrating superior performance in simulations and real cryptocurrency markets.

Contribution

It presents a novel approach integrating tick-level data and periodic signals using deep reinforcement learning for improved market making.

Findings

Outperforms existing methods in profitability

Enhances risk management in market making

Effective in both simulated and real cryptocurrency markets

Abstract

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.