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Machine Learning for Algorithmic Trading is a professional book on quantitative finance that provides an in-depth exploration of the end-to-end application of machine learning in algorithmic trading. This book details how to use machine learning technology to optimize trading strategies, including data preprocessing and feature engineering from market, fundamental and alternative data (such as high-frequency data, SEC filings, earnings call transcripts, financial news and even satellite images), especially how to study and evaluate financial features or Alpha factors. The book covers a wide range of machine learning techniques, from linear models and tree-based ensemble methods to deep learning (such as CNN, RNN, autoencoders), natural language processing (NLP), and generative adversarial networks (GANs), etc., and explains in detail how to design, train, optimize models, and conduct strategy backtesting. This book not only contains rich theoretical knowledge, but also provides a large number of practical cases and code examples using Python libraries (such as pandas, scikit-learn, TensorFlow, Zipline, backtrader, Alphalens, pyfolio, etc.), aiming to help readers convert machine learning model predictions into actual trading strategies, and effectively evaluate their performance and optimize portfolios.
Machine Learning for Algorithmic Trading 是一本关于量化金融的专业书籍，深入探讨了机器学习在算法交易中的端到端应用。本书详细介绍了如何利用机器学习技术来优化交易策略，包括从市场、基本面和另类数据（如高频数据、SEC文件、财报电话会议记录、金融新闻甚至卫星图像）中进行数据预处理和特征工程，特别是如何研究和评估金融特征或Alpha因子。书中涵盖了广泛的机器学习技术，从线性模型、基于树的集成方法到深度学习（如CNN、RNN、自编码器）、自然语言处理（NLP）以及生成对抗网络（GANs）等，并详细阐述了如何设计、训练、优化模型以及进行策略回测。本书不仅包含丰富的理论知识，还提供了大量实际案例和使用Python库（如pandas, scikit-learn, TensorFlow, Zipline, backtrader, Alphalens, pyfolio等）的代码示例，旨在帮助读者将机器学习模型预测转化为实际的交易策略，并有效评估其表现和进行投资组合优化。
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Online supplementary materials
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: Stefan Jansen
出版社: Packt Publishing
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 Algorithmic Trading.pdf)