Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
arXiv:2606.04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address this need, this research introduces novel concepts. To construct a robust system, we developed a hierarchical "Filter-then-Rank" pai
The increasing maturity and volatility of cryptocurrency markets, coupled with advancements in Deep Reinforcement Learning, are creating new opportunities for sophisticated trading strategies.
This research demonstrates the potential for AI-driven strategies to significantly enhance financial market operations in high-variance environments, impacting how trading is conducted and capital is allocated.
The application of DRL to multi-pair trading in volatile markets introduces adaptive and robust execution overlays, moving beyond rigid classical strategies.
- · AI/ML researchers in finance
- · Quantitative trading firms
- · Cryptocurrency exchanges
- · Early adopters of DRL strategies
- · Traditional algorithmic trading systems
- · Manual cryptocurrency traders
- · Less adaptive trading strategies
Increased efficiency and profitability of pair trading in cryptocurrency markets through DRL.
Broader adoption of DRL and other advanced AI techniques for financial market prediction and execution across various asset classes.
Potential for DRL systems to democratize sophisticated trading strategies, impacting market structure and liquidity.
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Read at arXiv cs.LG