《交易机器学习代码》第三版——从数据来源到实时执行。
概览
这个仓库托管了Stefan Jansen的《交易机器学习》第三版代码——一个从零开始重建的版本，围绕一个端到端的工作流程组织：如何定义一个研究理念，并迭代将其发展成一个你在实时市场中实际运行并持续运行的策略。 - 九个案例研究展示了贯穿全书27章的工作流程，从原始数据到特征、模型、回测、成本和风险再到部署。- 是本版新引入的案例，跨越了该工作流程，将检索增强生成、知识图谱和多代理系统引入金融研究。- 配套网站包含112本入门书、61个代理技能和6个生产Python库，支持工作流程的大部分内容。 加入《Navigate ML for Trading》第三版，获取30分钟的图书地图、案例研究、代码及配套资源。查看所有当前课程和研讨会;同学课程列表在下方课程类别。
概述（AI 翻译）
构建、测试并部署机器学习驱动的交易策略，从数据来源到实时执行。
机器学习交易，第三版的代码由 斯特凡·扬森：27章9个案例研究，重建 从零开始，围绕一个端到端的工作流程。研究想法如何成为策略 跑，继续跑，在一个活生生的市场里跑。
在找第二版吗？ 它在second-edition分支上完整且稳定：
git checkout second-edition，一切都和书中描述的完全一样。
目录
- 第三版的新内容
- 九个案例研究，本版结构核心
- 课程与研讨会
- 《密码》，逐章，27个章节指南
- 快速启动：安装、下载数据、运行你的第一本笔记本
- 配套资源：入门书、特工技能和六大库
- 仓库布局
第三版有什么新内容
整本书追踪一条路径：从数据基础设施到战略研究，跨越证据边界 将调优与评估分离，部署和监控，并通过反馈回路重新训练、暂停或 当策略的边缘衰减时，会退役。
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
Machine Learning for Trading, 3rd Edition
Build, test, and deploy ML-driven trading strategies, from data sourcing to live execution.
The code for Machine Learning for Trading, 3rd Edition by Stefan Jansen: 27 chapters and nine case studies, rebuilt from the ground up around one end-to-end workflow. How a research idea becomes a strategy you can run, and keep running, in a live market.
Looking for the second edition? It is complete and stable on the second-edition branch:
git checkout second-edition, and everything is exactly where that book describes it.
Contents
- What’s new in the third edition
- Nine case studies, the structural centerpiece of this edition
- Courses and workshops
- The code, chapter by chapter, 27 chapter guides
- Quick start: install, download the data, run your first notebook
- Companion resources: primers, agent skills, and the six libraries
- Repository layout
What’s New in the Third Edition
The whole book traces one path: from data infrastructure and strategy research, across an evidence boundary that separates tuning from evaluation, to deployment and monitoring, with a feedback loop that retrains, pauses, or retires a strategy as its edge decays.
Where earlier editions moved technique by technique, the third edition runs that one process end to end, and adds substantial new material:
- A wider model toolkit: from gradient boosting (XGBoost, LightGBM, CatBoost) to deep time-series architectures (PatchTST, iTransformer, TSMixer, TCN, Mamba) and newer tabular and latent-factor models (TabPFN, TabM, conditional and supervised autoencoders).
- Dedicated strategy-design chapters: transaction costs and risk management are now full chapters, neither of which existed before, joining portfolio construction and strategy synthesis so a raw signal is carried through to a sized, cost- and risk-aware portfolio.
- A full production track: live trading systems (Interactive Brokers, Alpaca, QuantConnect), MLOps and governance (drift detection, safe rollout, circuit breakers, feature stores, experiment tracking), and the operational reality of running strategies, not just building them.
- Generative AI: retrieval-augmented generation grounded in SEC filings, knowledge graphs and Graph RAG, and
推荐工具
换一个关键词，或者移除筛选条件。
安装
npx skillfish add stefan-jansen/machine-learning-for-trading