摘 要
随着金融科技的快速发展，大数据技术在量化投资领域的应用日益广泛，为提升投资决策效率和收益水平提供了新的可能性。本研究以大数据驱动的量化投资模型优化为核心，旨在通过整合多源数据和先进算法，构建更加精准、高效的量化投资策略。研究基于机器学习和统计分析方法，选取了涵盖市场行情、宏观经济指标、新闻舆情等多维度的大规模数据集，设计并实现了多层次特征提取与模型训练框架。通过引入深度学习模型和强化学习算法，有效提升了对非线性关系和动态市场环境的捕捉能力。实验结果表明，优化后的模型在预测准确性和风险控制方面均表现出显著优势，尤其是在复杂市场条件下的适应性更强。
关键词：量化投资 大数据技术 深度学习
Abstract
With the rapid development of fintech, big data technology is increasingly widely used in the field of quantitative investment, which provides new possibilities for improving the efficiency of investment decision-making and the income level. This study takes the big data-driven quantitative investment model optimization as the core, and aims to build a more accurate and efficient quantitative investment strategy by integrating multi-source data and advanced algorithms. Based on machine learning and statistical analysis methods, the paper selects large-scale data sets covering market conditions, macroeconomic indicators, news and public opinion, and realizes the multi-level feature extraction and model training fr amework. By introducing the deep learning model and the reinforcement learning algorithm, the ability to capture the nonlinear relationship and the dynamic market environment is effectively improved. The experimental results show that the optimized model showed significant advantages in both prediction accuracy and risk control, especially more adaptable in complex market conditions.
Keyword:Quantitative Investment Big Data Technology Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2大数据在量化投资中的应用基础 2
2.1大数据技术的核心要素 2
2.2量化投资模型的基本框架 3
2.3数据驱动的投资决策机制 3
2.4大数据对传统模型的改进作用 4
3量化投资模型优化的关键技术 4
3.1特征选择与降维方法研究 4
3.2机器学习算法的应用分析 5
3.3高频数据处理的技术挑战 5
3.4模型参数调优策略探讨 6
3.5不确定性环境下的鲁棒性设计 6
4实证研究与案例分析 7
4.1数据集构建与预处理方法 7
4.2投资组合优化的实证研究 7
4.3不同市场条件下的模型表现评估 7
4.4风险管理策略的优化效果分析 8
4.5结果验证与改进方向探讨 8
结论 9
参考文献 10
致谢 11
