用CNN+LSTM+DQN模型自动选股交易，有效应对市场噪声和复杂性。
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading
- 用CNN处理技术指标图像，LSTM捕捉时间序列依赖
- DQN根据特征学习最优买卖持策略，提升决策精度
- 适合对量化交易和深度强化学习感兴趣的从业者
本项目针对自动化股票交易中的挑战——传统方法与直接强化学习在市场噪声、复杂性和泛化能力方面表现不佳。提出一种集成深度学习框架：使用卷积神经网络（CNN）识别以图像形式呈现的技术指标中的模式，利用长短期记忆网络（LSTM）捕捉价格历史与技术指标间的时间依赖关系，并通过深度Q网络（DQN）代理基于CNN与LSTM提取的特征，学习最优交易策略（买入、卖出、持有），实现更稳健的决策。该方法在模拟环境中验证了其在复杂市场条件下的有效性。
原文摘要 · Abstract (English)
This project addresses the challenge of automated stock trading, where traditional methods and direct reinforcement learning (RL) struggle with market noise, complexity, and generalization. Our proposed solution is an integrated deep learning framework combining a Convolutional Neural Network (CNN) to identify patterns in technical indicators formatted as images, a Long Short-Term Memory (LSTM) network to capture temporal dependencies across both price history and technical indicators, and a Deep Q-Network (DQN) agent which learns the optimal trading policy (buy, sell, hold) based on the features extracted by the CNN and LSTM.
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