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

ArXiv ID: 2306.17179 “View on arXiv”

Authors: Unknown

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.

Keywords: Deep Reinforcement Learning, Market Making, High-Frequency Trading, Tick-Level Data, Liquidity Provision, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics like Hamilton-Jacobi-Bellman equations and deep reinforcement learning algorithms, indicating high complexity, while demonstrating empirical rigor through backtesting on real cryptocurrency data and simulation scenarios.

  flowchart TD
    A["Research Goal:<br>Develop Robust<br>High-Frequency Market Making Strategy"] --> B["Data Inputs:<br>Tick-level Data &<br>Periodical Signals"]
    B --> C["Methodology:<br>Deep Reinforcement Learning<br>with Data Fusion"]
    C --> D["Computational Process:<br>Simulation &<br>Real-world Experiments"]
    D --> E["Outcomes:<br>Outperforms Existing Methods<br>in Profitability & Risk Management"]