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用于自适应股票交易的多指标A2C

文章 arXiv papers · 作者: Jingfeng Pan et al.
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总结

本文介绍QTMRL，这是一种利用技术指标进行股票交易决策的强化学习智能体。其输入数据集结合了跨行业代表性标普500成分股的每日价格和成交量数据，以及旨在捕捉趋势、波动率和动量的特征。优势演员评论家框架将数据处理、策略学习和交易动作联系起来，旨在随着市场环境变化调整决策。

论文报告了该方法在不同市场环境下与统计模型、神经网络和移动平均基准的比较，并声称其在盈利能力、风险调整后表现和下行风险控制方面具有优势。描述未提供指标数值、详细实验流程、交易成本假设，也未提供超出所述样本范围的表现证据。数据集覆盖较长的历史时期，但这些股票和日期的结果未必能推广至其他资产或实盘交易。因此，该摘要有助于了解系统设计，但其稳健性和可部署性仍不明确。

核心观点

  • 该智能体将趋势、波动率和动量指标与强化学习相结合。
  • 其策略使用优势演员评论家框架选择交易动作。
  • 输入数据涵盖多个行业的精选标普500成分股每日观测值。
  • 论文将该系统与统计模型、神经网络和移动平均基准进行比较。
  • 描述中的绩效说法缺少数值结果和实盘交易证据。

标签

股票技术指标机器学习风险管理回测
全文
# QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning


# QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning









In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empirical rules often fail to adapt to dynamic market changes and black swan events due to rigid assumptions and limited generalization. To address these issues, this paper proposes QTMRL (Quantitative Trading Multi-Indicator Reinforcement Learning), an intelligent trading agent combining multi-dimensional technical indicators with reinforcement learning (RL) for adaptive and stable portfolio management. We first construct a comprehensive multi-indicator dataset using 23 years of S&P 500 daily OHLCV data (2000-2022) for 16 representative stocks across 5 sectors, enriching raw data with trend, volatility, and momentum indicators to capture holistic market dynamics. Then we design a lightweight RL framework based on the Advantage Actor-Critic (A2C) algorithm, including data processing, A2C algorithm, and trading agent modules to support policy learning and actionable trading decisions. Extensive experiments compare QTMRL with 9 baselines (e.g., ARIMA, LSTM, moving average strategies) across diverse market regimes, verifying its superiority in profitability, risk adjustment, and downside risk control. The code of QTMRL is publicly available at https://github.com/ChenJiahaoJNU/QTMRL.git

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。