用模仿学习+强化学习，让金融策略自适应应对变化市场。
FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
- 先学多个专家策略，再在噪声空间中强化优化生成动作序列。
- 在多变市场下表现优于单独优化的专家策略。
- 适合需要动态调整的量化交易或风险管理场景。
金融领域的传统随机控制方法因依赖简化假设，在真实市场中表现不佳，通常仅在特定、稳定环境下有效，而在非平稳变化环境中效果下降。本文提出FinFlowRL框架，用于金融最优随机控制。该框架首先从多个专家策略中预训练一个自适应元策略，随后通过在噪声空间中的强化学习进行微调，以优化生成过程。通过采用动作分块（action chunking）生成动作序列而非单一决策，有效应对市场的非马尔可夫特性。实验表明，FinFlowRL在多种市场条件下持续超越单独优化的专家策略。
原文摘要 · Abstract (English)
Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。