LoadingLoading
This book is organized by [TheQuantHackers Community] (https://llmquant.com/) and provides PDF downloads. It is only for learning and communication. The copyright belongs to the original author.
This book is organized by [TheQuantHackers Community] () and provides PDF downloads. It is only for learning and communication. The copyright belongs to the original author.
This book is written by Marcos López de Prado, an expert in quantitative finance, and systematically introduces the innovative application of machine learning in the field of asset management. It is designed to help asset managers use machine learning tools to discover economic and financial theories and overcome the limitations of traditional statistical methods in dealing with complex financial systems. The book covers core chapters such as machine learning basics, portfolio optimization, factor selection methods, risk management techniques, and strategy backtesting framework.
This book provides an in-depth exploration of a variety of mathematical techniques and their applications in finance, including but not limited to data denoising, distance measures, optimal clustering, financial labeling, feature importance analysis, and portfolio construction. It emphasizes the advantages of machine learning in improving out-of-sample prediction capabilities, handling non-linear relationships and high-dimensional data, and provides practical calculation methods and Python code examples to help readers apply theory to practice to make more informed decisions in asset management.
Python code implementation
Research datasets
Strategy backtesting tool
本书籍由TheQuantHackers社区整理, 并提供PDF下载, 只供学习交流使用, 版权归原作者所有。
作者: Marcos López de Prado
出版社: Cambridge University Press
出版年份: 2020
难度: ⭐⭐⭐⭐⭐
推荐指数: ⭐⭐⭐⭐⭐
PDF下载: [点击下载](https://asset.quant-wiki.com/pdf/Machine Learning for Asset Managers-Cambridge University Press (2020).pdf)
本书由量化金融专家Marcos López de Prado撰写，系统介绍了机器学习在资产管理领域的创新应用。它旨在帮助资产管理者利用机器学习工具发现经济和金融理论，克服传统统计方法在处理复杂金融系统时的局限性。书中涵盖了机器学习基础、投资组合优化、因子选择方法、风险管理技术以及策略回测框架等核心章节。
本书深入探讨了多种数学技术及其在金融领域的应用，包括但不限于数据去噪、距离度量、最优聚类、金融标签、特征重要性分析以及投资组合构建。 它强调了机器学习在提升样本外预测能力、处理非线性关系和高维数据方面的优势，并提供了实用的计算方法和Python代码示例，旨在帮助读者将理论应用于实践，从而在资产管理中做出更明智的决策。
Upgrade to unlock all institutional-grade algorithms, derivative pricing engines, factor backtesting frameworks, and live QuantLab execution.
Explore Membership Access
Pre-order · £490Quantitative analytics platform
Waitlist closed — signal locked. Pre-order TQH TERMINAL now at £490 with launch-day delivery.