摘 要
随着金融市场的复杂性和数据规模的快速增长，传统量化投资策略逐渐面临模型局限性和计算效率不足的问题，而机器学习技术凭借其强大的数据处理能力和模式识别优势，为量化投资领域提供了新的解决方案。本研究旨在探索机器学习在量化投资策略中的应用潜力，并通过优化算法提升策略表现。研究首先梳理了机器学习与量化投资结合的理论基础，分析了监督学习、无监督学习及强化学习等方法在资产定价、风险控制和交易信号生成中的适用性。随后，基于真实市场数据，构建了多种机器学习驱动的投资策略模型，包括基于随机森林的特征选择模型、长短期记忆网络（LSTM）的时间序列预测模型以及深度强化学习的动态交易决策模型。实验结果表明，相较于传统统计方法，机器学习模型能够更准确地捕捉市场非线性特征，显著提高收益风险比。
关键词：机器学习 量化投资 深度强化学习
Abstract
With the complexity of financial market and the rapid growth of data scale, traditional quantitative investment strategies are gradually faced with the problems of model limitations and insufficient computing efficiency, while machine learning technology provides new solutions in the quantitative investment field with its powerful data processing ability and pattern recognition advantages. This study aims to explore the potential of machine learning in quantitative investment strategies and improve strategy performance through optimization algorithms. The research first combs the theoretical basis of the combination of machine learning and quantitative investment, and analyzes the applicability of supervised learning, unsupervised learning and reinforcement learning in asset pricing, risk control and transaction signal generation. Subsequently, based on real market data, a variety of machine learning-driven investment strategy models were constructed, including feature selection model based on random forest, time series prediction model of long and short-term memory network (LSTM), and dynamic transaction decision model of deep reinforcement learning. Experimental results show that machine learning models can capture the nonlinear characteristics of the market more accurately and significantly improve the risk-off ratio.
Keyword:Machine Learning Quantitative Investment Deep Reinforcement Learning
目 录
1绪论 1
1.1量化投资与机器学习的背景分析 1
1.2研究意义与价值探讨 1
1.3国内外研究现状综述 1
1.4研究方法与技术路线 2
2机器学习在量化投资中的基础理论 2
2.1量化投资的核心概念与框架 2
2.2常见机器学习算法概述 3
2.3数据处理与特征工程的重要性 3
2.4模型评估与优化的基本原则 3
2.5理论框架对实践的指导作用 4
3机器学习在量化策略构建中的应用 4
3.1趋势预测模型的设计与实现 4
3.2风险管理中的机器学习方法 5
3.3高频交易中的算法优化策略 5
3.4组合优化中的多目标学习应用 6
3.5实证案例分析与结果讨论 6
4机器学习驱动的量化投资优化研究 7
4.1模型过拟合问题及其解决方案 7
4.2数据偏差对策略性能的影响分析 7
4.3强化学习在动态策略调整中的应用 8
4.4可解释性提升与投资者信任建立 8
4.5未来发展方向与潜在挑战 9
结论 9
参考文献 11
致谢 12
