ML交易-第二版 旨在说明ML如何以实用而全面的方式为算法交易策略增加价值。 它涵盖了从线性回归到深度强化学习的各种机器学习技术，并演示了如何建立，回测和评估由模型预测驱动的交易策略。 本书分为四个部分，共23章，另加附录，涵盖800余页： 数据采购，财务功能工程和资产组合管理的重要方面， 基于监督和无监督的机器学习算法的多空策略的设计和评估， 如何从SEC文件，收益电话记录或财务新闻等财务文本数据中提取可交易信号， 使用带有市场和替代数据的CNN和RNN等深度学习模型，如何使用生成的对抗网络生成综合数据，以及使用深度强化学习来训练交易代理 此回购包含150多个笔记本，这些笔记本将书中讨论的概念，算法和用例付诸实践。 他们提供了许多例子，说明 如何处理市场，基本和替代文本和图像数据并从中提取信号， 如何训练和调整可预测不同资产类别和投资范围的回报的模型，包括如何复制最近发表的

by Stefan Jansen Packt Publishing 2018-12-31 684 pages Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental

使用Python进行算法交易 克里斯·康兰（Chris Conlan）的《 Python算法交易》（2020）的源代码。 可以购买平装本。 有用的资源 无论有没有附带的书，这些独立的资源对于研究人员都是有用的。该存储库中的其余材料取决于书中给出的解释和上下文。 用于评估交易策略的绩效指标： 纯熊猫的常见技术指标： 将常见技术指标转换为三元信号： 用于数值优化的通用网格搜索包装器： 用于投资组合模拟的面向对象的构建基块： 用于多核重复K折交叉验证的通用包装器： 免费使用的模拟EOD库存数据和替代数据流：

Overview Market microstructure World markets Orders Algorithm overview Transaction costs Optimal trading strategies Order placement Execution tactics Enhancing trading strategies Infrastructure requirements Portfolios Multi-asset trading News Data mining and artificial intelligence

Successful Algorithmic Trading 的中英文版本，以及相关的代码。主要介绍可以回测，实盘的量化开发的流程，主要语言是python。也介绍了量化平台开发中的涉及到的各种坑。比如如何避免未来函数的回测等。也涉及到现在流行的机器学习算法在量化交易的应用等。

很经典的程序化交易入门书，关于高频交易HTF

successful-algorithmic-trading中文与英文版(含代码）

advanced-algorithimic-trading英文版包含代码

老头子考夫曼关于构建程式交易的成功要诀.

Quantitative Trading - How to Build Your Own Algorithmic Trading Business.