用多指标强化学习打造自适应交易代理，应对市场波动与黑天鹅事件
QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
- 融合23年美股数据与16个行业股票的多维度技术指标，构建动态市场表征
- 基于A2C算法的轻量框架在9种基准上实现更高收益与更优风险控制
- 适合量化交易研究者与机构投资者参考实战策略设计
在高度波动和不确定的全球金融市场中，依赖统计建模或经验规则的传统量化交易模型常因假设僵化和泛化能力有限，难以应对市场动态变化和黑天鹅事件。本文提出QTMRL（基于多指标引导强化学习的量化交易），一种结合多维技术指标与强化学习的智能交易代理，实现自适应且稳定的组合管理。首先，利用2000-2022年23年标普500日度OHLCV数据，涵盖16只代表性股票、5个行业，构建综合多指标数据集，通过趋势、波动率和动量等指标丰富原始数据，捕捉全面的市场动态。随后设计基于优势演员-评论家（A2C）算法的轻量级强化学习框架，包含数据处理、A2C算法与交易代理模块，支持策略学习与可执行交易决策。大量实验在不同市场环境下对比了QTMRL与9种基线方法（如ARIMA、LSTM、移动平均策略），验证其在盈利能力、风险调整及下行风险控制方面的优越性。QTMRL代码已公开于https://github.com/ChenJiahaoJNU/QTMRL.git。
原文摘要 · Abstract (English)
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
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