<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>金融工程 on Text Matrix</title><link>https://txtmix.com/tags/%E9%87%91%E8%9E%8D%E5%B7%A5%E7%A8%8B/</link><description>Recent content in 金融工程 on Text Matrix</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Tue, 21 Jul 2026 20:06:14 +0800</lastBuildDate><atom:link href="https://txtmix.com/tags/%E9%87%91%E8%9E%8D%E5%B7%A5%E7%A8%8B/index.xml" rel="self" type="application/rss+xml"/><item><title>Stefan Jansen《Machine Learning for Trading》2nd：量化金融 ML 工程化完全指南</title><link>https://txtmix.com/posts/tech/stefan-jansen-machine-learning-for-trading-guide/</link><pubDate>Tue, 02 Jun 2026 03:05:00 +0800</pubDate><guid>https://txtmix.com/posts/tech/stefan-jansen-machine-learning-for-trading-guide/</guid><description>&lt;h1 id="stefan-jansenmachine-learning-for-trading2nd量化金融-ml-工程化完全指南">Stefan Jansen《Machine Learning for Trading》2nd：量化金融 ML 工程化完全指南&lt;/h1>
&lt;p>量化交易的机器学习资源长期割裂成两端：学术论文重理论、与代码脱节，券商研报重策略、数据封闭。Stefan Jansen 的《Machine Learning for Algorithmic Trading》第 2 版是少数同时给出完整代码与系统框架的工程化手册——150+ Jupyter Notebooks 覆盖 23 章、800+ 页，从数据源、特征工程、监督/无监督模型，一路走到 NLP（自然语言处理）、深度学习和强化学习的回测落地。&lt;/p></description></item></channel></rss>