Abstract
This research paper delves into the application of Deep Reinforcement Learning (DRL) in asset-class agnostic portfolio optimization, integrating industry-grade methodologies with quantitative finance. At the heart of this integration is our robust framework that not only merges advanced DRL algorithms with modern computational techniques but also emphasizes stringent statistical analysis, software engineering and regulatory compliance. To the best of our knowledge, this is the first study integrating financial Reinforcement Learning with sim-to-real methodologies from robotics and mathematical physics, thus enriching our frameworks and arguments with this unique perspective. Our research culminates with the introduction of AlphaOptimizerNet, a proprietary Reinforcement Learning agent (and corresponding library). Developed from a synthesis of state-of-the-art (SOTA) literature and our unique interdisciplinary methodology, AlphaOptimizerNet demonstrates encouraging risk-return optimization across various asset classes with realistic constraints. These preliminary results underscore the practical efficacy of our frameworks. As the finance sector increasingly gravitates towards advanced algorithmic solutions, our study bridges theoretical advancements with real-world applicability, offering a template for ensuring safety and robust standards in this technologically driven future.
Abstract (translated)
本文深入研究了在资产类别无关的组合优化中应用深度强化学习（DRL）的方法，将行业级别的方法和量化金融相结合。这一整合的核心是我们的稳健框架，不仅将先进的DRL算法与现代计算技术相结合，而且强调了严格的统计分析、软件工程和法规合规性。据我们所知，这是第一个将金融强化学习与机器人学和数学物理中的模拟到现实方法相结合的研究，从而丰富了我们框架和论点的独特视角。我们的研究最后引入了AlphaOptimizerNet，一种专有强化学习代理（相应库）。作为最先进的文献综述和独特跨学科方法的结果，AlphaOptimizerNet在各种资产类别的风险收益优化方面表现出鼓舞人心的效果。这些初步结果强调了我们在框架中的实际有效性。随着金融部门越来越倾向于采用先进的人工智能解决方案，我们的研究将理论进步与现实应用相结合，为在技术驱动的未来确保安全和稳健标准提供了模板。
URL
https://arxiv.org/abs/2403.07916
