AI用深度强化学习炒股，比传统方法更稳赚
Can Artificial Intelligence Trade the Stock Market?
- 用DDQN和PPO算法学炒股，避开不利时机
- 2019-2023年数据验证，风险调整后收益显著提升
- 适合对量化交易和AI选股感兴趣的投资者
本文研究深度强化学习（DRL）在股票市场交易中的应用，重点对比双深度Q网络（DDQN）和近端策略优化（PPO）两种算法与买入持有基准的表现。评估覆盖三个货币对、标普500指数及比特币，基于2019至2023年的日度数据。结果表明，DRL能有效进行交易并主动规避不利行情，实现更高的风险调整后回报，优于基于监督学习的传统方法。
原文摘要 · Abstract (English)
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.
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