Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

Problem
This study addresses the limitations of classical pair trading strategies when applied to the highly volatile cryptocurrency markets, which often lead to rigidity and significant divergence risks. The authors highlight the gap in the literature regarding the application of Deep Reinforcement Learning (DRL) in this context, particularly as existing methods have not effectively adapted to the unique challenges posed by digital assets. The work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors propose a hierarchical “Filter-then-Rank” pair selection methodology combined with a “Fixed Risk, Adaptive Mean” execution model. The core technical contribution is the implementation of a Proximal Policy Optimization (PPO) agent augmented with a Long Short-Term Memory (LSTM) layer, which governs execution decisions while adhering to strict deterministic risk management protocols. The training utilized 1-hour interval data from the Binance USD-M Futures market, focusing on optimizing the RL policy to enhance performance metrics. The architecture is designed to mitigate divergence risks by anchoring the neural policy to statistically robust boundaries, thus ensuring safer execution in volatile environments.

Results
The proposed DRL-based trading strategy demonstrated significant out-of-sample performance improvements over a heuristic baseline. Specifically, the optimized RL policy achieved a risk-adjusted return that was statistically significant at the 10% level, although it did not meet the stricter 5% significance threshold. The results underscore the agent’s ability to navigate the extreme idiosyncratic variance typical of cryptocurrency markets, as confirmed by a stationary circular block bootstrap robustness check.

Limitations
The authors acknowledge that while the results are promising, the statistical significance falls short of the conventional 5% threshold, indicating potential concerns regarding the robustness of the findings. Additionally, the study is limited to a specific dataset from the Binance USD-M Futures market, which may not generalize across other cryptocurrency exchanges or asset classes. The reliance on a single execution model may also restrict the adaptability of the approach to different market conditions.

Why it matters
This research contributes to the quantitative finance literature by integrating DRL with statistical arbitrage techniques, offering a novel framework for safe reinforcement learning through deterministic shielding. The implications of this work extend to the development of more resilient trading strategies in cryptocurrency markets, potentially influencing future research on adaptive trading systems. The findings suggest that anchoring neural policies to robust statistical boundaries can effectively mitigate risks associated with high volatility, paving the way for further exploration in this domain, as published in arXiv cs.NE.