← 研踪 PaperTrailarXiv:2611.09012 · v1
Towards Robust Reinforcement Learning Market Making: Lessons from Loss Function Experimentation
首次提交 2026-11-06 13:42
版本更新 2026-11-06 13:42
雷达观测 2026-11-09 11:19
摘要
Market makers play a vital role in ensuring efficient trade within financial markets. Recent literature has delved into training market-making systems using reinforcement learning. This paper delves into the impact of various loss function formulations on risk management behavior within reinforcement-based market making. Through empirical evaluation, we contrast profit-maximizing strategies with risk-averse approaches, finding that penalties for inventory and cash management foster more resilient and adaptable market-making strategies. Our results underscore the significance of striking a balance between profit maximization and risk management in algorithmic trading, providing actionable insights for refining strategies in practical settings.
作者备注：6 pages, 3 figures
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v1 · 2026-11-06 13:42 · 最新观测版本
Towards Robust Reinforcement Learning Market Making: Lessons from Loss Function Experimentation
Market makers play a vital role in ensuring efficient trade within financial markets. Recent literature has delved into training market-making systems using reinforcement learning. This paper delves into the impact of various loss function formulations on risk management behavior within reinforcement-based market making. Through empirical evaluation, we contrast profit-maximizing strategies with risk-averse approaches, finding that penalties for inventory and cash management foster more resilient and adaptable market-making strategies. Our results underscore the significance of striking a balance between profit maximization and risk management in algorithmic trading, providing actionable insights for refining strategies in practical settings.
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